Items with Index Term: neural network

A. Mohy, A.; Bassioni, H. A.; Elgendi, E. O. and Hassan, T. M. (2026) Modeling spatiotemporal hazard dynamics for construction safety using graph attention networks. Journal of Engineering, Design and Technology, ISSN 1726-0531

Abbasnejad, B; Nasirian, A; Duan, S; Diro, A; Prasad Nepal, M and Song, Y (2024) Measuring BIM implementation: A mathematical modeling and artificial neural network approach. Journal of Construction Engineering and Management, 150(5): 04024032, ISSN 0733-9364

Abdel-Galil, E; Ibrahim, A H and Alborkan, A (2022) Assessment of transaction costs for construction projects. International Journal of Construction Management, 22(9), pp. 1618-1631. ISSN 1562-3599

AbdelRazig, Y A (1999) Construction quality assessment: A hybrid decision support model using image processing and neural learning for intelligent defects recognition. PhD thesis, Purdue University, USA.

AbdelRazig, Y A and Chang, L M (2000) Intelligent model for constructed facilities surface assessment. Journal of Construction Engineering and Management, 126(6), pp. 422-432. ISSN 0733-9364

Abdelkader, E M; Moselhi, O; Marzouk, M and Zayed, T (2021) Integrative evolutionary-based method for modeling and optimizing budget assignment of bridge maintenance priorities. Journal of Construction Engineering and Management, 147(9): 04021100, ISSN 0733-9364

AbouRizk, S M and Wales, R J (1997) Combined discrete-event/continuous simulation for project planning. Journal of Construction Engineering and Management, 123(1), pp. 11-20. ISSN 0733-9364

Abounia Omran, B (2016) Application of data mining and big data analytics in the construction industry. PhD thesis, Ohio State University, USA.

Abu Dabous, S; Ibrahim, F and Alzghoul, A (2025) Modelling bridge deterioration using long short-term memory neural networks: A deep learning-based approach. Smart and Sustainable Built Environment, 14(5), pp. 1632-1655. ISSN 2046-6099

Abuhishmeh, K S K (2023) Risk assessment of reinforced concrete sewer pipes under external loading and adverse environmental conditions using an adaptive neuro-fuzzy system. PhD thesis, University of Texas at Arlington, USA.

Adeli, H and Wu, M (1998) Regularization neural network for construction cost estimation. Journal of Construction Engineering and Management, 124(1), pp. 18-23. ISSN 0733-9364

Adul-Hamid, R (1996) Construction duration prediction using neural network methodology. PhD thesis, University of Manchester, UK.

Afzal, Muneeb (2024) Improving request for information (RFI) processing in construction projects using natural language processing (NLP) techniques. PhD thesis, University of Technology Sydney, Australia.

Ahiaga-Dagbui, D and Smith, S D (2012) Neural networks for modelling the final target cost of water projects. In: Smith, S D (ed.) Proceedings of 28th Annual ARCOM Conference, 3-5 September 2012, Edinburgh, UK.

Ahiaga-Dagbui, D D (2014) Rethinking construction cost overruns: an artificial neural network approach to construction cost estimation. PhD thesis, University of Edinburgh, UK.

Ahiaga-Dagbui D D, T O and Smith S D, W S (2013) A neuro-fuzzy hybrid model for predicting final cost of water infrastructure projects. In: Smith, S D and Ahiaga-Dagbui, D D (eds.) Proceedings of 29th Annual ARCOM Conference, 2-4 September 2013, Reading, UK.

Aibinu, A A; Dassanayake, D; Chan, T-K and Thangaraj, R (2015) Cost estimation for electric light and power elements during building design: A neural network approach. Engineering, Construction and Architectural Management, 22(2), pp. 190-213. ISSN 0969-9988

Akinsola, A O (1997) An intelligent model of variations' contingency on constructions projects. PhD thesis, University of Wolverhampton, UK.

Al Omari, Mohammad K (2025) Application of the AI-enabled unmanned aerial systems in resolving highway construction claims. PhD thesis, University of Illinois at Chicago, USA.

Al-Sobiei, O S (2001) Assessment of risk allocation in construction projects. PhD thesis, Illinois Institute of Technology, USA.

Al-Tabtabai, H; Kartam, N; Flood, I A N and Alex, A P (1997) Expert judgment in forecasting construction project completion. Engineering, Construction and Architectural Management, 4(4), pp. 271-293. ISSN 0969-9988

Al-Zahrani, J I (2013) The impact of contractors' attributes on construction project success. PhD thesis, University of Manchester, UK.

Aldaghi, T and Javanmard, S (2023) The evaluation of wastewater treatment plant performance: A data mining approach. Journal of Engineering, Design and Technology, 21(6), pp. 1785-1802. ISSN 1726-0531

Alex, D P; Al Hussein, M; Bouferguene, A and Fernando, S (2010) Artificial neural network model for cost estimation: City of Edmonton's water and sewer installation services. Journal of Construction Engineering and Management, 136(7), pp. 745-756. ISSN 0733-9364

Algarni, S K (2022) Machine learning for improved detection and segmentation of building boundary. PhD thesis, Cardiff University, UK.

Alikhani, H; Le, C; Jeong, H D and Damnjanovic, I (2023) Sequential machine learning for activity sequence prediction from daily work report data. Journal of Construction Engineering and Management, 149(9): 04023082, ISSN 0733-9364

Alipouri, Y; Bokharaeian, A and Monfared, E (2025) Construction labor activity recognition via an imu-implemented wristband. Journal of Construction Engineering and Management, 151(11): 04025182, ISSN 0733-9364

Alqahtani, A and Whyte, A (2013) Artificial neural networks incorporating cost significant items towards enhancing estimation for (life-cycle) costing of construction projects. Construction Economics and Building, 13(3), pp. 51-64. ISSN 2204-9029

Alshboul, O; Shehadeh, A; Al-Kasasbeh, M; Al Mamlook, R E; Halalsheh, N and Alkasasbeh, M (2022) Deep and machine learning approaches for forecasting the residual value of heavy construction equipment: A management decision support model. Engineering, Construction and Architectural Management, 29(10), pp. 4153-4176. ISSN 0969-9988

Alsugair, A M (1992) An intelligent resource allocation system. PhD thesis, Texas A&M University, USA.

Altun, M (2024) Data-driven and knowledge-assisted model-based frameworks for supporting facility maintenance. PhD thesis, Middle East Technical University, Turkey.

Alves, J. L.; Almeida Filho, A. T. D.; Bradaschia, F. and Palha, R. P. (2026) Solarisbim.AI: Smart sustainable building planning with BIM-based solar-production estimation using machine-learning radiation forecasts. Journal of Construction Engineering and Management, 152(6): 04026076, ISSN 0733-9364

Alzubi, K. M.; Alaloul, W. S.; Al Salaheen, M.; Musarat, M. A.; Baarimah, A. O. and Mushtaha, A. W. (2025) Indoor construction productivity assessment using computer vision and mask region-based convolutional neural networks. Construction Innovation, 26(5), pp. 1523-1555. ISSN 1471-4175

Andersen, T O M and Gaarslev, A (1996) Perspectives on artificial intelligence in the construction industry. Engineering, Construction and Architectural Management, 3(1-2), pp. 3-14. ISSN 0969-9988

Art Chaovalitwongse, W; Wang, W; Williams, T P and Chaovalitwongse, P (2012) Data mining framework to optimize the bid selection policy for competitively bid highway construction projects. Journal of Construction Engineering and Management, 138(2), pp. 277-286. ISSN 0733-9364

Ashtab, M and Ryoo, B Y (2022) Predicting construction workforce demand using a combination of feature selection and multivariate deep-learning seq2seq models. Journal of Construction Engineering and Management, 148(12): 04022136, ISSN 0733-9364

Attalla, M M A M (2000) Reconstruction of operating facilities: A model for project management. PhD thesis, University of Waterloo, Canada.

Awad, A and Fayek, A R (2013) Adaptive learning of contractor default prediction model for surety bonding. Journal of Construction Engineering and Management, 139(6), pp. 694-704. ISSN 0733-9364

Awad, A L S (2012) Intelligent contractor default prediction model for surety bonding in the construction industry. PhD thesis, University of Alberta, Canada.

Badawy, M; Hussein, A; Elseufy, S M and Alnaas, K (2021) How to predict the rebar labours' production rate by using ANN model. International Journal of Construction Management, 21(4), pp. 427-438. ISSN 1562-3599

Bae, J (2025) Supervised learning to covering cost risk through post-construction evaluation of transportation projects by project delivery methods. Engineering, Construction and Architectural Management, 32(9), pp. 5863-5884. ISSN 0969-9988

Bakheet, M T (1995) Contractors' risk assessment system. PhD thesis, Georgia Institute of Technology, USA.

Bala, K; Bustani, S A and Waziri, B S (2014) A computer-based cost prediction model for institutional building projects in Nigeria: An artificial neural network approach. Journal of Engineering, Design and Technology, 12(4), pp. 519-530.

Barros, Natalia Nakamura (2024) Integrative model of life cycle assessment with internet of things, BIM and machine learning. PhD thesis, Universidade Estadual de Campinas, Brazil.

Bates, A J (2008) The owner's role in project success. PhD thesis, Polytechnic University, USA.

Bayram, S and Al-Jibouri, S (2016) Efficacy of estimation methods in forecasting building projects' costs. Journal of Construction Engineering and Management, 142(11): 05016012, ISSN 0733-9364

Bee-Hua, G (2000) Evaluating the performance of combining neural networks and genetic algorithms to forecast construction demand: The case of the Singapore residential sector. Construction Management and Economics, 18(2), pp. 209-217. ISSN 01446193

Benjaoran, V; Dawood, N and Scott, D (2004) Bespoke precast productivity estimation with neural network model. In: Khosrowshahi, F (ed.) Proceedings of 20th Annual ARCOM Conference, 1-3 September 2004, Edinburgh, UK.

Bhasha, A V and Reddy, B D V (2022) A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-spectral images. Journal of Engineering, Design and Technology, 20(6), pp. 1538-1564. ISSN 1726-0531

Bokor, O (2022) Improving labour productivity in construction. A hybrid machine learning approach. PhD thesis, University of Northumbria at Newcastle, UK.

Boussabaine, A H and Duff, A R (1996) An expert-simulation system for construction productivity forecasting. Building Research & Information, 24(5), pp. 279-286. ISSN 0961-3218

Boussabaine, A H and Kaka, A P (1998) A neural networks approach for cost flow forecasting. Construction Management and Economics, 16(4), pp. 471-479. ISSN 01446193

Boussabaine, A H; Kirkham, R J and Grew, R G (1999) Estimating the cost of energy usage in sport centres: A comparative modelling approach. In: Hughes, W (ed.) Proceedings of 15th Annual ARCOM Conference, 15-17 September 1999, Liverpool, UK.

Boussabaine, A H; Thomas, R and Elhag, T M S (1999) Modelling cost-flow forecasting for water pipeline projects using neural networks. Engineering, Construction and Architectural Management, 6(3), pp. 213-224. ISSN 0969-9988

Budayan, C; Dikmen, I and Birgonul, T (2007) Strategic group analysis by using self organizing maps. In: Boyd, D (ed.) Proceedings of 23rd Annual ARCOM Conference, 3-5 September 2007, Belfast, UK.

Cai, J; Yang, L; Zhang, Y; Li, S and Cai, H (2021) Multitask learning method for detecting the visual focus of attention of construction workers. Journal of Construction Engineering and Management, 147(7): 04021063, ISSN 0733-9364

Cao, M T; Cheng, M Y and Wu, Y W (2015) Hybrid computational model for forecasting Taiwan construction cost index. Journal of Construction Engineering and Management, 141(4): 04014089, ISSN 0733-9364

Carmo, Cristiano Saad Travassos do (2023) A hybrid solution using stochastic and neural networks modeling for the consideration of safety uncertainties in construction planning methods. PhD thesis, Pontifícia Universidade Católica do Rio de Janeiro, Brazil.

Chan, A P C; Chan, D W M and Yeung, J F Y (2009) Overview of the application of "fuzzy techniques" in construction management research. Journal of Construction Engineering and Management, 135(11), pp. 1241-1252. ISSN 0733-9364

Chao, L C (2010) Estimating project overheads rate in bidding: DSS approach using neural networks. Construction Management and Economics, 28(3), pp. 287-299. ISSN 1466433X

Chao, L C and Chien, C F (2009) Estimating project S-curves using polynomial function and neural networks. Journal of Construction Engineering and Management, 135(3), pp. 169-177. ISSN 0733-9364

Chao, L C and Kuo, C P (2018) Neural-network-centered approach to determining lower limit of combined rate of overheads and markup. Journal of Construction Engineering and Management, 144(2): 04017117, ISSN 0733-9364

Chao, L C and Skibniewski, M J (1995) Neural network method of estimating construction technology acceptability. Journal of Construction Engineering and Management, 121(1), pp. 130-142. ISSN 0733-9364

Chao, L-C (1994) An adaptive approach to evaluation of implementation prospects of new construction technologies. PhD thesis, Purdue University, USA.

Chen, C (2007) Soft computing-based life-cycle cost analysis tools for transportation infrastructure management. PhD thesis, Virginia Polytechnic Institute and State University, USA.

Chen, C; Zhang, Y; Xiao, B; Cheng, M; Zhang, J and Li, H (2024) Deep learning-based image steganography for visual data cybersecurity in construction management. Journal of Construction Engineering and Management, 150(10): 04024125, ISSN 0733-9364

Chen, F-C (2021) Deep learning studies for vision-based condition assessment and attribute estimation of civil infrastructure systems. PhD thesis, Purdue University, USA.

Chen, J-H (2003) Litigation prediction model for construction disputes caused by change orders. PhD thesis, University of Wisconsin - Madison, USA.

Chen, Kaiwen (2020) Analysis and management of UAV-captured images towards automation of building facade inspections. PhD thesis, Virginia Tech, USA.

Chen, W T and Huang, Y H (2006) Approximately predicting the cost and duration of school reconstruction projects in Taiwan. Construction Management and Economics, 24(12), pp. 1231-1239. ISSN 1466433X

Chen, Z; Li, T; Qin, L and Jiang, Y (2025) Vision-guided autonomous block loading in a dual-robot collaborative handling framework. Journal of Construction Engineering and Management, 151(5): 04025035, ISSN 0733-9364

Cheng, M. Y.; Sholeh, M. N. and Poetra, B. A. (2026) Predicting the construction cost-time tradeoff using optimized hybrid deep learning for risk preference decision making. International Journal of Construction Management, 26(10), pp. 2018-2044. ISSN 1562-3599

Cheng, M Y; Chang, Y H and Korir, D (2019) Novel approach to estimating schedule to completion in construction projects using sequence and nonsequence learning. Journal of Construction Engineering and Management, 145(11): 04019072, ISSN 0733-9364

Cheng, M Y and Ko, C H (2003) Object-oriented evolutionary fuzzy neural inference system for construction management. Journal of Construction Engineering and Management, 129(4), pp. 461-469. ISSN 0733-9364

Cheung, S; Wong, P S P; Fung, A Y S and Coffey, W V (2008) Examining the use of bid information in predicting the contractor's performance. Journal of Financial Management of Property and Construction, 13(2), pp. 111-122. ISSN 1366-4387

Chew, M Y L; De Silva, N and Tan, S S (2004) A neural network approach to assessing building façade maintability in the tropics. Construction Management and Economics, 22(6), pp. 581-594. ISSN 0144-6193

Ching-Lung, F (2024) Using convolutional neural networks to identify illegal roofs from unmanned aerial vehicle images. Architectural Engineering and Design Management, 20(2), pp. 390-410. ISSN 1745-2007

Chokwitthaya, C (2020) A framework for augmenting building performance models using machine learning and immersive virtual environment. PhD thesis, Louisiana State University and Agricultural & Mechanical College, USA.

Christodoulou, S E (1998) Optimum bid markup calculation in competitive bidding environments using fuzzy artificial neural networks. PhD thesis, Columbia University, USA.

Chua, D K H; Kog, Y C and Loh, P K (1999) Critical success factors for different project objectives. Journal of Construction Engineering and Management, 125(3), pp. 142-150. ISSN 0733-9364

Chua, D K H; Kog, Y C; Loh, P K and Jaselskis, E J (1997) Model for construction budget performance: neural network approach. Journal of Construction Engineering and Management, 123(3), pp. 214-222. ISSN 0733-9364

Cirilovic, J; Vajdic, N; Mladenovic, G and Queiroz, C (2014) Developing cost estimation models for road rehabilitation and reconstruction: Case study of projects in Europe and Central Asia. Journal of Construction Engineering and Management, 140(3): 4013065, ISSN 0733-9364

Commuri, S; Mai, A T and Zaman, M (2011) Neural network-based intelligent compaction analyzer for estimating compaction quality of hot asphalt mixes. Journal of Construction Engineering and Management, 137(9), pp. 634-644. ISSN 0733-9364

Dang-Trinh, N; Duc-Thang, P; Nguyen-Ngoc Cuong, T and Duc-Hoc, T (2023) Machine learning models for estimating preliminary factory construction cost: Case study in southern Vietnam. International Journal of Construction Management, 23(16), pp. 2879-2887. ISSN 1562-3599

Darko, A; Glushakova, I; Boateng, E B and Chan, A P C (2023) Using machine learning to improve cost and duration prediction accuracy in green building projects. Journal of Construction Engineering and Management, 149(8): 04023061, ISSN 0733-9364

Demirkesen, S (2024) Prediction of number of insured having work accident in Turkish construction industry. Proceedings of Institution of Civil Engineers: Management, Procurement and Law, 177(4), pp. 193-206. ISSN 17514304

Deng, T; Sharafat, A; Lee, S and Seo, J (2024) Automatic vision-based dump truck productivity measurement based on deep-learning illumination enhancement for low-visibility harsh construction environment. Journal of Construction Engineering and Management, 150(11): 04024154, ISSN 0733-9364

Dias, J L; Silva, A; Chai, C; Gaspar, P L and De Brito, J (2014) Neural networks applied to service life prediction of exterior painted surfaces. Building Research & Information, 42(3), pp. 371-380. ISSN 0961-3218

Dikmen, I and Birgonul, M T (2004) Neural network model to support international market entry decisions. Journal of Construction Engineering and Management, 130(1), pp. 59-66. ISSN 0733-9364

Dikmen, I; Birgonul, M T and Kiziltas, S (2005) Prediction of organizational effectiveness in construction companies. Journal of Construction Engineering and Management, 131(2), pp. 252-261. ISSN 0733-9364

Duan, P; Zhou, J and Fan, W (2024) Safety tag generation and training material recommendation for construction workers: A persona-based approach. Engineering, Construction and Architectural Management, 31(1), pp. 115-135. ISSN 0969-9988

Duff, A R; Emsley, M; Gregory, M; Lowe, D and Masterman, J (1998) Development of a model of total building procurement costs for construction clients. In: Hughes, W (ed.) Proceedings of 14th Annual ARCOM Conference, 9-11 September 1998, Reading, UK.

Dunston, P S and Bernold, L E (2000) Adaptive control for safe and quality rebar fabrication. Journal of Construction Engineering and Management, 126(2), pp. 122-129. ISSN 0733-9364

Dursun, O and Stoy, C (2016) Conceptual estimation of construction costs using the multistep ahead approach. Journal of Construction Engineering and Management, 142(9): 04016038, ISSN 0733-9364

Edwards, D J; Yang, J; Cabahug, R and Love, P E D (2005) Intelligence and maintenance proficiency: An examination of plant operators. Construction Innovation, 5(4), pp. 243-254. ISSN 1471-4175

El Sawalhi, N I H (2007) Developing a model for construction contractors pre-qualification in the Gaza Strip and West Bank. PhD thesis, University of Salford, UK.

El-Kholy, A M (2021) Exploring the best ANN model based on four paradigms to predict delay and cost overrun percentages of highway projects. International Journal of Construction Management, 21(7), pp. 694-712. ISSN 1562-3599

El-Sawalhi, N; Eaton, D and Rustom, R (2008) Forecasting contractor performance using a neural network and genetic algorithm in a pre-qualification model. Construction Innovation, 8(4), pp. 280-298. ISSN 1471-4175

Elavaar Kuzhali S and Pushpa M K, P (2024) Adaptive deep learning for deep COVID-19 diagnosis. Journal of Engineering, Design and Technology, 22(3), pp. 763-794. ISSN 1726-0531

Elazouni, A M (2006) Classifying construction contractors using unsupervised-learning neural networks. Journal of Construction Engineering and Management, 132(12), pp. 1242-1253. ISSN 0733-9364

Elazouni, A M; Ali, A E and Abdel-Razek, R H (2005) Estimating the acceptability of new formwork systems using neural networks. Journal of Construction Engineering and Management, 131(1), pp. 33-41. ISSN 0733-9364

Elghaish, F; Matarneh, S; Abdellatef, E; Rahimian, F; Hosseini, M R and Farouk Kineber, A (2025) Multi-layers deep learning model with feature selection for automated detection and classification of highway pavement cracks. Smart and Sustainable Built Environment, 14(2), pp. 511-535. ISSN 2046-6099

Elghaish, F; Matarneh, S T; Talebi, S; Abu-Samra, S; Salimi, G and Rausch, C (2022) Deep learning for detecting distresses in buildings and pavements: A critical gap analysis. Construction Innovation, 22(3), pp. 554-579. ISSN 1471-4175

Elghaish, F; Talebi, S; Abdellatef, E; Matarneh, S T; Hosseini, M R; Wu, S; Mayouf, M; Hajirasouli, A and Nguyen, T Q (2022) Developing a new deep learning CNN model to detect and classify highway cracks. Journal of Engineering, Design and Technology, 20(4), pp. 993-1014. ISSN 1726-0531

Elhag, T M S (2004) Tender price modelling: artificial neural networks and regression techniques. PhD thesis, University of Liverpool, UK.

Elhag, T M S and Boussabaine, A H (2001) Tender price estimation using artificial neural networks. Journal of Financial Management of Property and Construction, 6(3), pp. 193-208. ISSN 1366-4387

Elhag, T M S and Boussabaine, A H (2002) Tender price estimation using artificial neural networks II: Modelling. Journal of Financial Management of Property and Construction, 7(1), pp. 49-64. ISSN 1366-4387

Elnady, Ahmed Abdelrady Okasha Mohamed (2023) An integrated framework for balancing contractor's workload versus capacity using system dynamics. PhD thesis, University of Alberta, Canada.

Elshaboury, N; Mohammed Abdelkader, E and Al-Sakkaf, A (2025) Convolutional neural network-based deep learning model for air quality prediction in october city of Egypt. Construction Innovation, 25(2), pp. 620-640. ISSN 1471-4175

Elshaboury, N; Mohammed Abdelkader, E; Al-Sakkaf, A and Bagchi, A (2025) A deep convolutional neural network for predicting electricity consumption at grey nuns building in Canada. Construction Innovation, 25(2), pp. 270-289. ISSN 1471-4175

Emsley, M W (2001) A model to optimize single tower crane location within a construction site. PhD thesis, Loughborough University, UK.

Emsley, M W; Lowe, D J; Duff, A R; Harding, A and Hickson, A (2002) Data modelling and the application of a neural network approach to the prediction of total construction costs. Construction Management and Economics, 20(6), pp. 465-472. ISSN 01446193

Ensafi, Mahnaz (2022) Work order prioritization using neural networks to improve building operation. PhD thesis, Virginia Tech, USA.

Espada, R; Apan, A and McDougall, K (2017) Vulnerability assessment of urban community and critical infrastructures for integrated flood risk management and climate adaptation strategies. International Journal of Disaster Resilience in the Built Environment, 8(4), pp. 375-411. ISSN 1759-5916

Ezeldin, A S and Sharara, L M (2006) Neural networks for estimating the productivity of voncreting activities. Journal of Construction Engineering and Management, 132(6), pp. 650-656. ISSN 0733-9364

Fahmy, K A; Yahya, A and Zorkany, M (2022) A decision support healthcare system based on IoT and neural network technique. Journal of Engineering, Design and Technology, 20(3), pp. 727-748. ISSN 1726-0531

Fan, X (2024) Artificial intelligence aided resilient and sustainable water infrastructure systems. PhD thesis, Case Western Reserve University, USA.

Farouk, A M and Rahman, R A (2025) Integrated applications of building information modeling in project cost management: A systematic review. Journal of Engineering, Design and Technology, 23(1), pp. 287-305. ISSN 1726-0531

Fernando, N; T.A, K D and Zhang, H (2024) An artificial neural network (ann) approach for early cost estimation of concrete bridge systems in developing countries: The case of Sri Lanka. Journal of Financial Management of Property and Construction, 29(1), pp. 23-51. ISSN 1366-4387

Forbes, D R; McGurnaghan, H; Graham, L D and Smith, S D (2004) Concrete placing productivity using a novel neural network design. In: Khosrowshahi, F (ed.) Proceedings of 20th Annual ARCOM Conference, 1-3 September 2004, Edinburgh, UK.

Fu, J; Tian, H; Song, L; Li, M; Bai, S and Ren, Q (2021) Productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data. Engineering, Construction and Architectural Management, 28(7), pp. 2023-2041. ISSN 0969-9988

García de Soto, B (2014) A methodology to make accurate preliminary estimates of construction material quantities for construction projects. DSc thesis, ETH Zürich, Switzerland.

García de Soto, B; Adey, B T and Fernando, D (2017) A hybrid methodology to estimate construction material quantities at an early project phase. International Journal of Construction Management, 17(3), pp. 165-196. ISSN 1562-3599

Georgy, M E; Chang, L M and Zhang, L (2005) Prediction of engineering performance: A neurofuzzy approach. Journal of Construction Engineering and Management, 131(5), pp. 548-557. ISSN 0733-9364

Ghahari, S (2021) Detecting and measuring corruption and inefficiency in infrastructure projects using machine learning and data analytics. PhD thesis, Purdue University, USA.

Goh, Y M and Binte Sa'Adon, N F (2015) Cognitive factors influencing safety behavior at height: A multimethod exploratory study. Journal of Construction Engineering and Management, 141(6): 04015003, ISSN 0733-9364

Goh, Y M and Chua, D (2013) Neural network analysis of construction safety management systems: A case study in Singapore. Construction Management and Economics, 31(5), pp. 460-470. ISSN 1466433X

Golabchi, H and Hammad, A (2024) Estimating labor resource requirements in construction projects using machine learning. Construction Innovation, 24(4), pp. 1048-1065. ISSN 1471-4175

Graham, L D and Smith, S D (2004) A method for effectively implementing construction process productivity estimation models. In: Khosrowshahi, F (ed.) Proceedings of 20th Annual ARCOM Conference, 1-3 September 2004, Edinburgh, UK.

Grey Rodriguez, F C (2019) Space-mate: A framework to harmonize occupant well-being and building sustainability. PhD thesis, Stanford University, USA.

Halaweh, M M (2012) System management of construction projects based on industrial engineering prespective with impact on net profit. PhD thesis, State University of New York at Binghamton, USA.

Hamayat, F.; Ahmad, R. F.; Ghaban, W.; Saeed, F.; Ahmad, J.; Anwar, S. M. and Zubair, S. (2026) Data-driven stnet and stprophet models for secure edge-based indoor air temperature prediction in smart buildings. Building Research & Information, 54(4), pp. 492-509. ISSN 0961-3218

Hamoda, M F (2008) Modeling of construction noise for environmental impact assessment. Journal of Construction in Developing Countries, 13(1), pp. 79-89. ISSN 1823-6499

Han, S (2005) Application modeling of the conventional and the GPS-based earthmoving systems. PhD thesis, Purdue University, USA.

Harding, A; Lowe, D; Hickson, A; Emsley, M and Duff, A R (1999) Implementation of a neural network model for the comparison of the cost of different procurement approaches. In: Hughes, W (ed.) Proceedings of 15th Annual ARCOM Conference, 15-17 September 1999, Liverpool, UK.

Hassim, S; Muniandy, R; Alias, A H and Abdullah, P (2018) Construction tender price estimation standardization (TPES) in Malaysia: Modeling using fuzzy neural network. Engineering, Construction and Architectural Management, 25(3), pp. 443-457. ISSN 0969-9988

He, R (2023) Modeling of sustainable materials management systems: Hybrid science-based, data-driven approaches. PhD thesis, Carnegie Mellon University, USA.

Hegazy, T and Ayed, A (1998) Neural network model for parametric cost estimation of highway projects. Journal of Construction Engineering and Management, 124(3), pp. 210-218. ISSN 0733-9364

Hegazy, T M (1994) Integrated bid preparation with emphases on risk assessment using neural networks. PhD thesis, Concordia University, Canada.

Hellas, M S; Chaib, R and Verzea, I (2020) Artificial intelligence treating the problem of uncertainty in quantitative risk analysis (QRA). Journal of Engineering, Design and Technology, 18(1), pp. 40-54. ISSN 1726-0531

Heravi, G and Eslamdoost, E (2015) Applying artificial neural networks for measuring and predicting construction-labor productivity. Journal of Construction Engineering and Management, 141(10): 04015032, ISSN 0733-9364

Heravi, M Y; Jang, Y and Chauhan, H (2025) Comparative analysis of deep-learning approaches for automatic recognition of awkward postures in construction environments through wrist-worn biosensors. Journal of Construction Engineering and Management, 151(8): 04025108, ISSN 0733-9364

Hogan, D B (1998) Modeling construction cost performance: A comprehensive approach using statistical, artificial neural network and simulation methods. PhD thesis, Columbia University, USA.

Holt, G D (1997) Classifying construction contractors: A case study using cluster analysis. Building Research & Information, 25(6), pp. 374-382. ISSN 0961-3218

Hong, J (2006) A study on analytic approaches to intelligent buildings assessment. PhD thesis, Hong Kong Polytechnic University, Hong Kong.

Hong, Ying Civil (2018) Modelling and evaluating the adoption of building information modelling in Australian and Chinese small and medium-sized construction organisations. PhD thesis, University of New South Wales, Australia.

Hsiao, W T; Yu, W D and Shih, C C (2026) Proactive construction hazard prevention model using machine learning. Journal of Construction Engineering and Management, 152(2): 04025261, ISSN 0733-9364

Hsie, M (1994) Computer-aided acceptance planning: Generating quality acceptance parameters and stratified sampling plans through neural network learning ability and CAD modeling. PhD thesis, Purdue University, USA.

Hu, Q; Bai, Y; He, L; Cai, Q; Tang, S; Ma, G; Tan, J and Liang, B (2020) Intelligent framework for worker-machine safety assessment. Journal of Construction Engineering and Management, 146(5): 04020045, ISSN 0733-9364

Hussain, M A D (2001) Value engineering expert system in suburban highway design (VEESSHD). PhD thesis, University of Pittsburgh, USA.

Idan, C K (2003) Quality function deployment (QFD) in the UK construction industry. PhD thesis, Nottingham Trent University, UK.

Iliescu, S (2000) A case-based reasoning approach to the designing of building envelopes. PhD thesis, Concordia University, Canada.

Isied, M M (2023) Critical assessment of asphalt mixture design procedures and asphalt mixture classification systems. PhD thesis, North Carolina State University, USA.

Jang, Y; Jeong, I B; Cho, Y K and Ahn, Y (2019) Predicting business failure of construction contractors using long short-term memory recurrent neural network. Journal of Construction Engineering and Management, 145(11): 04019067, ISSN 0733-9364

Jang, Y; Kim, K; Leite, F; Ayer, S and Cho, Y K (2021) Identifying the perception differences of emerging construction-related technologies between industry and academia to enable high levels of collaboration. Journal of Construction Engineering and Management, 147(10): 06021004, ISSN 0733-9364

Jassim, H S H (2019) Assessing energy use and carbon emissions to support planning of environmentally sustainable earthmoving operations. PhD thesis, Luleå University of Technology, Sweden.

Jezzini, Y; Assaad, R H and El-Adaway, I H (2025) Modeling framework to quantify and gauge project cost risks due to construction material price volatilities using predictive probabilistic deep-learning algorithms and stochastic risk modeling. Journal of Construction Engineering and Management, 151(7): 04025071, ISSN 0733-9364

Jha, K N and Chockalingam, C T (2011) Prediction of schedule performance of Indian construction projects using an artificial neural network. Construction Management and Economics, 29(9), pp. 901-911. ISSN 1466433X

Ji, A; Xue, X; Zhang, L; Luo, X and Man, Q (2025) A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification. Engineering, Construction and Architectural Management, 32(4), pp. 2455-2486. ISSN 0969-9988

Jiang, Q (2020) Estimation of construction project building cost by back-propagation neural network. Journal of Engineering, Design and Technology, 18(3), pp. 601-609. ISSN 1726-0531

Jiang, Y (2020) Determination of elevations for excavation operations using drone technologies. PhD thesis, Marquette University, USA.

Jiang, Y and Bai, Y (2020) Estimation of construction site elevations using drone-based orthoimagery and deep learning. Journal of Construction Engineering and Management, 146(8): 04020086, ISSN 0733-9364

Jiang, Z; Han, Y; Cheng, Y; Wang, Z and Meng, H (2025) An improved yolov8-dyhead-wiseiou model for positioning and counting detection of grouting sleeves in a prefabricated wall. Journal of Construction Engineering and Management, 151(4): 04025016, ISSN 0733-9364

Jin, R; Zuo, J and Hong, J (2019) Scientometric review of articles published in ASCE's journal of construction engineering and management from 2000 to 2018. Journal of Construction Engineering and Management, 145(8): 06019001, ISSN 0733-9364

Jonnalagadda, S (2016) Artificial neural networks, non linear auto regression networks (NARX) and causal loop diagram approaches for modelling bridge infrastructure conditions. PhD thesis, Clemson University, USA.

Joy Vasantha Rani, S P and Aruna Prabha, K (2010) Stochastic logic computation based rbfnn with adaptive hidden layer structure. Journal of Engineering, Design and Technology, 8(2), pp. 206-220. ISSN 1726-0531

Ju, J.; Liu, X.; Xu, F.; Cao, B.; Chen, W.; Wang, X. and Tian, G. (2026) Operational phase recognition and power matching under shoveling conditions in an autonomous loading mobile concrete mixer. Journal of Construction Engineering and Management, 152(9): 04026131, ISSN 0733-9364

Jupp, B C (2003) Forecasting value in building projects. PhD thesis, Loughborough University, UK.

Kale, S and Karaman, E A (2011) Evaluating the knowledge management practices of construction firms by using importance-comparative performance analysis maps. Journal of Construction Engineering and Management, 137(12), pp. 1142-1152. ISSN 0733-9364

Kang, L; Liu, S; Zhang, H and Gong, D (2021) Person anomaly detection-based videos surveillance system in urban integrated pipe gallery. Building Research & Information, 49(1), pp. 55-68. ISSN 0961-3218

Kantianis, D D (2022) Design morphology complexity and conceptual building project cost forecasting. Journal of Financial Management of Property and Construction, 27(3), pp. 387-414. ISSN 1366-4387

Karaiskos, P; Munian, Y; Martinez-Molina, A and Alamaniotis, M (2026) Indoor air quality prediction modeling for a naturally ventilated fitness building using rnn-LSTM artificial neural networks. Smart and Sustainable Built Environment, 15(1), pp. 384-406. ISSN 2046-6099

Karami, S (2008) Using by-product industrial materials to replace all cement in construction products. PhD thesis, Coventry University, UK.

Khajuria, A (1994) Quality assurance of concrete using a hybrid knowledge-based expert system. PhD thesis, Rutgers State University of New Jersey, School of Graduate Studies, USA.

Khosrowshahi, F (1999) Neural network model for contractors' pre-qualification for local authority projects. Engineering, Construction and Architectural Management, 6(3), pp. 315-328. ISSN 0969-9988

Kim, J M; Yum, S G; Adhikari, M D and Bae, J (2024) A LSTM algorithm-driven deep learning approach to estimating repair and maintenance costs of apartment buildings. Engineering, Construction and Architectural Management, 31(13), pp. 369-389. ISSN 0969-9988

Kindangen, J I (1996) Artificial neural networks and naturally ventilated buildings. Building Research & Information, 24(4), pp. 203-208. ISSN 0961-3218

Ko, C H and Cheng, M Y (2007) Dynamic prediction of project success using artificial intelligence. Journal of Construction Engineering and Management, 133(4), pp. 316-324. ISSN 0733-9364

Kobayashi, Y (2001) Three-dimensional city modeler with fuzzy multiple layers perceptron: Application of soft computing in computer-aided architectural design systems. PhD thesis, University of California, Los Angeles, USA.

Korishetti, A. C. and Malemath, V. S. (2026) Optimal block search mechanism using deep recurrent neural network for enabling the code-efficiency in hevc. Journal of Engineering, Design and Technology, 24(4), pp. 35-62. ISSN 1726-0531

Kumar, S; Singh, L K; Roy, L B; Kumar, R and Lal, D (2025) Enhancing the sustainability of slope stability in embankment construction by leveraging smart sensors and monitoring systems for data-driven insights. International Journal of Construction Management, 25(16), pp. 2102-2120. ISSN 1562-3599

Lam, K C; Hu, T; Cheung, S O; Yuen, R K K and Deng, Z M (2001) Multi-project cash flow optimization: Non-inferior solution through neuro-multiobjective algorithm. Engineering, Construction and Architectural Management, 8(2), pp. 130-144. ISSN 0969-9988

Lam, K C; Hu, T; Ng, S T; Skitmore, M and Cheoung, S O (2001) A fuzzy neural network approach for contractor prequalification. Construction Management and Economics, 19(2), pp. 175-188. ISSN 01446193

Lam, K C; Hu, T S and Ng, S T (2005) Using the principal component analysis method as a tool in contractor pre-qualification. Construction Management and Economics, 23(7), pp. 673-684. ISSN 01446193

Lam, K C and Oshodi, O S (2016) Forecasting construction output: A comparison of artificial neural network and Box-Jenkins model. Engineering, Construction and Architectural Management, 23(3), pp. 302-322. ISSN 0969-9988

Lam, K C; Thomas Ng, S; Hu, T; Skitmore, M and Cheung, S O (2000) Decision support system for contractor pre-qualification: Artificial neural network model. Engineering, Construction and Architectural Management, 7(3), pp. 251-266. ISSN 0969-9988

Laura-Portugal, C. and Hammad, A. (2026) Deep learning-based forecasting for construction project duration at completion. International Journal of Construction Management, 26(8), pp. 1543-1561. ISSN 1562-3599

Lee, C and Park, K K H (2022) Forecasting trading volume in local housing markets through a time-series model and a deep learning algorithm. Engineering, Construction and Architectural Management, 29(1), pp. 165-178. ISSN 0969-9988

Lee, J; Blumenstein, M; Guan, H and Loo, Y C (2013) Minimising uncertainty in long-term prediction of bridge element. Engineering, Construction and Architectural Management, 20(2), pp. 127-142. ISSN 0969-9988

Li, J.; Chen, Z.; Li, Z.; Kong, L. and Zhang, H. (2026) Integrating computer vision and audio signals for concrete vibration activity recognition and assessment. Journal of Construction Engineering and Management, 152(7): 04026099, ISSN 0733-9364

Li, M N; Wang, X; Cheng, R X and Chen, Y (2025) Aided design decision-making framework for engineering projects considering cost and social benefits. Engineering, Construction and Architectural Management, 32(8), pp. 5040-5065. ISSN 0969-9988

Li, H (1996) Neural network models for intelligent support of mark-up estimation. Engineering, Construction and Architectural Management, 3(1-2), pp. 69-81. ISSN 0969-9988

Li, H; Shen, L Y and Love, P E D (1999) Ann-based mark-up estimation system with self-explanatory capacities. Journal of Construction Engineering and Management, 125(3), pp. 185-189. ISSN 0733-9364

Li, M (2024) Dfma-oriented rebar design optimization for reinforced concrete structures using graph neural network. PhD thesis, Hong Kong University of Science and Technology, Hong Kong.

Li, W (1995) Benefit and cost analysis: Three-dimensional computer models with integrated databases in the management of construction. PhD thesis, Columbia University, USA.

Liu, G.; Qin, Z.; Wu, H.; Jia, L. and Zhuo, J. (2026) Optimizing process efficiency in prefabricated building supply chain: The role of hybrid governance behaviors in reducing transaction costs. Engineering, Construction and Architectural Management, 33(5), pp. 3820-3842. ISSN 0969-9988

Liu, C; M.E. Sepasgozar, S; Shirowzhan, S and Mohammadi, G (2022) Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms. Construction Innovation, 22(1), pp. 141-159. ISSN 1471-4175

Liu, J; Liu, G; Wang, N; Pan, M and Jiang, Y (2025) Predicting structural deterioration of large-scale building clusters using snapshot data: An integrated Markov-LSTM model. Building Research & Information, 53(6), pp. 700-716. ISSN 0961-3218

Liu, M and Ling, Y Y (2005) Modeling a contractor's markup estimation. Journal of Construction Engineering and Management, 131(4), pp. 391-399. ISSN 0733-9364

Liu, Y C and Yeh, I C (2016) Building valuation model of enterprise values for construction enterprise with quantile neural networks. Journal of Construction Engineering and Management, 142(2): 04015075, ISSN 0733-9364

Lowe, D J; Emsley, M W and Harding, A (2007) Relationships between total construction cost and design related variables. Journal of Financial Management of Property and Construction, 12(1), pp. 11-24. ISSN 1366-4387

Lowe, D J; Parvar, J and Emsley, M W (2004) Development of a decision support system (DSS) for the contractor's decision to bid: Regression analysis and neural networks solutions. Journal of Financial Management of Property and Construction, 9(1), pp. 27-42. ISSN 1366-4387

Lu, M (2000) Productivity studies using advanced ANN models. PhD thesis, University of Alberta, Canada.

Lu, M (2002) Enhancing project evaluation and review technique simulation through artificial neural network-based input modeling. Journal of Construction Engineering and Management, 128(5), pp. 438-445. ISSN 0733-9364

Lu, Y and Zhang, J (2022) Bibliometric analysis and critical review of the research on big data in the construction industry. Engineering, Construction and Architectural Management, 29(9), pp. 3574-3592. ISSN 0969-9988

Luo, H (2021) Automated construction machine pose monitoring using computer vision and deep learning for construction site safety. PhD thesis, Hong Kong University of Science and Technology, Hong Kong.

Luo, X; Li, X; Song, X and Liu, Q (2023) Convolutional neural network algorithm-based novel automatic text classification framework for construction accident reports. Journal of Construction Engineering and Management, 149(12): 04023128, ISSN 0733-9364

Ma, Z; Liu, W; Li, C; Sang, Y; Zhang, Y; Li, G and Xu, Y (2024) Research on energy-saving control strategy of loader based on intelligent identification of working stages. Journal of Construction Engineering and Management, 150(7): 04024075, ISSN 0733-9364

Mahamedi, E; Wonders, M; Gerami Seresht, N; Woo, W L and Kassem, M (2024) A reinforcing transfer learning approach to predict buildings energy performance. Construction Innovation, 24(1), pp. 242-255. ISSN 1471-4175

Malekaee Ashtiyani, F.; Karimi Gavareshki, M. h and Gheidar-Kheljani, J. (2026) Real-time prediction of project duration deviation using CNN-LSTM-FIS. Built Environment Project and Asset Management, 16(2), pp. 304-320. ISSN 2044-124X

Mane, K M; Kulkarni, D K and Prakash, K B (2021) Prediction of shear strength of concrete produced by using pozzolanic materials and partly replacing nfa by MS using ann. Journal of Engineering, Design and Technology, 19(2), pp. 578-587. ISSN 1726-0531

Mansoor, A; Liu, S; Ali, G M; Bouferguene, A and Al-Hussein, M (2023) A deep-learning classification framework for reducing communication errors in dynamic hand signaling for crane operation. Journal of Construction Engineering and Management, 149(2): 4022167, ISSN 0733-9364

Mansoor, A; Liu, S; Bouferguene, A and Al-Hussein, M (2024) Crane signalman hand-signal classification framework using sensor-based smart construction glove and machine-learning algorithms. Journal of Construction Engineering and Management, 150(8): 04024094, ISSN 0733-9364

Mansuri, L E and Patel, D A (2022) Artificial intelligence-based automatic visual inspection system for built heritage. Smart and Sustainable Built Environment, 11(3), pp. 622-646. ISSN 2046-6099

Mao, Z (2003) Forecasting total factor productivity growth in the construction industry using neural network modelling. PhD thesis, National University of Singapore, Singapore.

Marzouk, M and Amin, A (2013) Predicting construction materials prices using fuzzy logic and neural networks. Journal of Construction Engineering and Management, 139(9), pp. 1190-1198. ISSN 0733-9364

Marzouk, M and Zaher, M (2020) Artificial intelligence exploitation in facility management using deep learning. Construction Innovation, 20(4), pp. 609-624. ISSN 1471-4175

Meharie, M G; Mengesha, W J; Gariy, Z A and Mutuku, R N N (2022) Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects. Engineering, Construction and Architectural Management, 29(7), pp. 2836-2853. ISSN 0969-9988

Meng, Q and Zhu, S (2022) Construction activity classification based on vibration monitoring data: A supervised deep-learning approach with time series randaugment. Journal of Construction Engineering and Management, 148(9): 04022090, ISSN 0733-9364

Mensah, I; Adjei-Kumi, T and Nani, G (2016) Duration determination for rural roads using the principal component analysis and artificial neural network. Engineering, Construction and Architectural Management, 23(5), pp. 638-656. ISSN 0969-9988

Miglioranza, P; Scanu, A; Simionato, G; Sinigaglia, N and Califano, A (2024) Machine learning and engineering feature approaches to detect events perturbing the indoor microclimate in ringebu and heddal stave churches (Norway). International Journal of Building Pathology and Adaptation, 42(1), pp. 35-47. ISSN 23984708

Milošević, I; Kovačević, M and Petronijević, P (2021) Estimating residual value of heavy construction equipment using ensemble learning. Journal of Construction Engineering and Management, 147(7): 04021073, ISSN 0733-9364

Modin, J (1995) KBS-class: A neural network tool for automatic content recognition of building texts. Construction Management and Economics, 13(5), pp. 411-416. ISSN 01446193

Mohammed Abdelkader, E; Zayed, T; Elshaboury, N and Taiwo, R (2025) A hybrid Bayesian optimization-based deep learning model for modeling the condition of saltwater pipes in Hong Kong. International Journal of Construction Management, 25(1), pp. 46-62. ISSN 1562-3599

Moon, S and Munira Chowdhury, A (2021) Utilization of prior information in neural network training for improving 28-day concrete strength prediction. Journal of Construction Engineering and Management, 147(5): 2047, ISSN 0733-9364

Moriyani, M A; Le, C; Le, T and Pirim, H (2026) Beyond one-size-fits-all: A novel data-driven framework for quantifying bidding competition intensity in highway design-bid-build contracts. Journal of Construction Engineering and Management, 152(3): 04026006, ISSN 0733-9364

Moselhi, O; Assem, I and El-Rayes, K (2005) Change orders impact on labor productivity. Journal of Construction Engineering and Management, 131(3), pp. 354-359. ISSN 0733-9364

Moselhi, O; Hegazy, T and Fazio, P (1991) Neural networks as tools in construction. Journal of Construction Engineering and Management, 117(4), pp. 606-625. ISSN 0733-9364

Moselhi, O; Hegazy, T and Fazio, P (1993) DBID: Analogy-based dss for bidding in construction. Journal of Construction Engineering and Management, 119(3), pp. 466-479. ISSN 0733-9364

Moselhi, O and Khan, Z (2010) Analysis of labour productivity of formwork operations in building construction. Construction Innovation, 10(3), pp. 286-303. ISSN 1471-4175

Moselhi, O and Khan, Z (2012) Significance ranking of parameters impacting construction labour productivity. Construction Innovation, 12(3), pp. 272-296. ISSN 1471-4175

Moteleb, M (2010) Risk based decision making tools for sewer infrastructure management. PhD thesis, University of Cincinnati, USA.

Mukherjee, K (2025) Cognitive abstractions for visual communication. PhD thesis, University of Wisconsin - Madison, USA.

Mund, A T (2002) An evaluation framework for exterior envelope wall systems for the homebuilding industry. PhD thesis, Arizona State University, USA.

Murtaza, M B (1993) A decision support model integrating neural networks and an expert system for construction modularization. PhD thesis, University of Houston, USA.

Nabawy, M and Gouda Mohamed, A (2024) Risks assessment in the construction of infrastructure projects using artificial neural networks. International Journal of Construction Management, 24(4), pp. 361-373. ISSN 1562-3599

Nasirzadeh, F; Kabir, H M D; Akbari, M; Khosravi, A; Nahavandi, S and Carmichael, D G (2020) ANN-based prediction intervals to forecast labour productivity. Engineering, Construction and Architectural Management, 27(9), pp. 2335-2351. ISSN 0969-9988

Nevett Fernández, G (2020) Duration estimators and productivity metrics for highway construction. PhD thesis, University of Colorado at Boulder, USA.

Ng, A K W and Price, A D F (2010) Optimizing the time performance of subcontractors in building projects. Construction Economics and Building, 10(1-2), pp. 90-102. ISSN 2204-9029

Ning, X; Shan, M; Li, Z and Nie, Z (2025) Prediction of construction material prices based on the sliding window catboost model. Journal of Construction Engineering and Management, 151(12): 04025196, ISSN 0733-9364

Odeyinka, H A; Lowe, J G and Kaka, A P (2002) A construction cost flow risk assessment model. In: Greenwood, D (ed.) Proceedings of 18th Annual ARCOM Conference, 2-4 September 2002, Northumbria, UK.

Oduyemi, O I (2015) Life cycle costing methodology for sustainable commerical office buildings. PhD thesis, University of Derby, UK.

Ogunlana, S O; Bhokha, S and Pinnemitr, N (2001) Application of artifical neural network (ANN) to forecast construction cost of buildings at the pre-design stage. Journal of Financial Management of Property and Construction, 6(3), pp. 179-192. ISSN 1366-4387

Ogunseiju, O R; Gonsalves, N; Akanmu, A A; Abraham, Y and Nnaji, C (2024) Automated detection of learning stages and interaction difficulty from eye-tracking data within a mixed reality learning environment. Smart and Sustainable Built Environment, 13(6), pp. 1473-1489. ISSN 2046-6099

Ogunseiju, O R; Olayiwola, J; Akanmu, A A and Nnaji, C (2022) Recognition of workers' actions from time-series signal images using deep convolutional neural network. Smart and Sustainable Built Environment, 11(4), pp. 812-831. ISSN 2046-6099

Ok, S C and Sinha, S K (2006) Construction equipment productivity estimation using artificial neural network model. Construction Management and Economics, 24(10), pp. 1029-1044. ISSN 1466433X

Ong, J (2019) Enabling lean through using shared mental models for precast construction in Singapore. PhD thesis, National University of Singapore, Singapore.

Oshodi, O S; Thwala, W D; Odubiyi, T B; Abidoye, R B and Aigbavboa, C O (2019) Using neural network model to estimate the rental price of residential properties. Journal of Financial Management of Property and Construction, 24(2), pp. 217-230. ISSN 1366-4387

Ottaviani, F M; De Marco, A; Narbaev, T and Ballesteros-Pérez, P (2026) Work rate-based indicators for improving project performance regression models. International Journal of Construction Management, 26(1), pp. 99-112. ISSN 1562-3599

Ottaviani, F M; De Marco, M; Audisio, A; Wong, J and Belack, C (2024) A brief review of artificial intelligence techniques for conceptual cost estimation in construction projects. In: Thomson, C (ed.) Proceedings of 40th Annual ARCOM Conference, 2-4 September 2024, London South Bank University, UK.

Palaneeswaran, E; Love, P E D; Kumaraswamy, M M and Ng, T S T (2008) Mapping rework causes and effects using artificial neural networks. Building Research & Information, 36(5), pp. 450-465. ISSN 0961-3218

Paris, D E (2002) A residential satisfaction decision support system for affordable housing. PhD thesis, Georgia Institute of Technology, USA.

Park, J (2017) Dynamic multi-dimensional BIM for total construction as-built documentation. PhD thesis, Purdue University, USA.

Parvar, J (2003) Neural networks decision support system (decision to bid). PhD thesis, University of Manchester, UK.

Parvar, J; Lowe, D; Emsley, M and Duff, A R (2000) Neural networks as a decision support system for the decision to bid process. In: Akintoye, A (ed.) Proceedings of 16th Annual ARCOM Conference, 6-8 September 2000, Glasgow, UK.

Patel, D A (2015) Estimating the number of fatal accidents and investigating the determinants of safety performance in indian construction. PhD thesis, Indian Institute of Technology Delhi, India.

Patel, D A and Jha, K N (2015) Neural network model for the prediction of safe work behavior in construction projects. Journal of Construction Engineering and Management, 141(1): 04014066, ISSN 0733-9364

Patil, S; Goudar, M and Kharadkar, R (2022) Neural network-based estimation of lighting condition in indoor environment with improved brain storm algorithm. Journal of Engineering, Design and Technology, 20(6), pp. 1565-1580. ISSN 1726-0531

Patil, S K (2016) Determining influencing factors and predicting dispute outcome of variation claims in Indian construction contracts/ by Smita Krishnarao Patil. PhD thesis, Indian Institute of Technology Delhi, India.

Perez-Perez, Y; Golparvar-Fard, M and El-Rayes, K (2021) Scan2bim-net: Deep learning method for segmentation of point clouds for scan-to-BIM. Journal of Construction Engineering and Management, 147(9): 04021107, ISSN 0733-9364

Petlíková, K (2021) Application of biological algorithms in standardization of construction work. PhD thesis, Czech Technical University, Czech Republic.

Petroutsatou, K; Georgopoulos, E; Lambropoulos, S and Pantouvakis, J P (2012) Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), pp. 679-687. ISSN 0733-9364

Pham, T Q D; Le-Hong, T and Tran, X V (2023) Efficient estimation and optimization of building costs using machine learning. International Journal of Construction Management, 23(5), pp. 909-921. ISSN 1562-3599

Portas, J and AbouRizk, S (1997) Neural network model for estimating construction productivity. Journal of Construction Engineering and Management, 123(4), pp. 399-410. ISSN 0733-9364

Poudel, O and Assaad, R H (2025) A real-time intelligent acoustic IoT-enabled embedded construction site monitoring and alert system: Integrating deep learning-based machine-listening algorithms, edge computing, and cloud computing. Journal of Construction Engineering and Management, 151(7): 04025075, ISSN 0733-9364

Pulket, T and Arditi, D (2009) Construction litigation prediction system using ant colony optimization. Construction Management and Economics, 27(3), pp. 241-251. ISSN 1466433X

Qi, K. and Lu, M. (2026) A data-driven framework for quantifying physical workload in construction using posture-hour metrics. Journal of Construction Engineering and Management, 152(7): 04026100, ISSN 0733-9364

Qiao, J; Wang, C; Guan, S and Shuran, L (2022) Construction-accident narrative classification using shallow and deep learning. Journal of Construction Engineering and Management, 148(9): 04022088, ISSN 0733-9364

Rafiei, M H and Adeli, H (2018) Novel machine-learning model for estimating construction costs considering economic variables and indexes. Journal of Construction Engineering and Management, 144(12): 04018106, ISSN 0733-9364

Rai, H; Jagannathan, M and Venkata Santosh Kumar, D (2021) Claim tenability assessment in Indian real estate projects using ann and decision tree models. Built Environment Project and Asset Management, 11(3), pp. 468-487. ISSN 2044-124X

Rajak, S and Vimal, V (2024) LSTM-CNN architecture for construction activity recognition using optimal positioning of wearables. Journal of Construction Engineering and Management, 150(12): 04024179, ISSN 0733-9364

Rakesh Kumar, G (2025) Seismic vulnerability assessment of buildings using rapid visual screening for hybrid UMNN-RFO approach. International Journal of Construction Management, 25(10), pp. 1177-1185. ISSN 1562-3599

Rankin, J (2023) Reinforcement learning for trench excavation. PhD thesis, Loughborough University, UK.

Rashad, A; Hegazy, H; Zhang, J; Mahdi, I; Abdel-Rasheed, I and Ebid, A (2025) Developing preliminary cost estimates for foundation systems of high-rise buildings. International Journal of Construction Management, 25(6), pp. 682-698. ISSN 1562-3599

Saadi, A and Belhadef, H (2020) Deep neural networks for Arabic information extraction. Smart and Sustainable Built Environment, 9(4), pp. 467-482. ISSN 2046-6099

Sakib, M N; Chaspari, T and Behzadan, A H (2022) A feedforward neural network for drone accident prediction from physiological signals. Smart and Sustainable Built Environment, 11(4), pp. 1017-1041. ISSN 2046-6099

Salman, A; Moselhi, O and Zayed, T (2013) Scheduling model for rehabilitation of distribution networks using MINLP. Journal of Construction Engineering and Management, 139(5), pp. 498-509. ISSN 0733-9364

Sandhaus, G (1998) Neural networks for cost estimating in project management. PhD thesis, Swansea University, UK.

Sanni-Anibire, M O; Zin, R M and Olatunji, S O (2022) Machine learning model for delay risk assessment in tall building projects. International Journal of Construction Management, 22(11), pp. 2134-2143. ISSN 1562-3599

Sawhney, A and Mund, A (2001) Intellicranes: An integrated crane type and model selection system. Construction Management and Economics, 19(2), pp. 227-237. ISSN 01446193

Sawhney, A and Mund, A (2002) Adaptive probabilistic neural network-based crane type selection system. Journal of Construction Engineering and Management, 128(3), pp. 265-273. ISSN 0733-9364

Seydel, J (2003) Evaluating and comparing bidding optimization effectiveness. Journal of Construction Engineering and Management, 129(3), pp. 285-292. ISSN 0733-9364

Shafaat, A (2016) Designing project systems in presence of variations. PhD thesis, Purdue University, USA.

Shamsollahi, D; Moselhi, O and Khorasani, K (2024) Automated detection and segmentation of mechanical, electrical, and plumbing components in indoor environments by using the yolact++ architecture. Journal of Construction Engineering and Management, 150(8): 04024100, ISSN 0733-9364

Sharma, V; Zaki, M; Jha, K N and Krishnan, N M A (2022) Machine learning-aided cost prediction and optimization in construction operations. Engineering, Construction and Architectural Management, 29(3), pp. 1241-1257. ISSN 0969-9988

Shehab, T and Farooq, M (2013) Neural network cost estimating model for utility rehabilitation projects. Engineering, Construction and Architectural Management, 20(2), pp. 118-126. ISSN 0969-9988

Sherafat, B; Ahn, C R; Akhavian, R; Behzadan, A H; Golparvar-Fard, M; Kim, H; Lee, Y C; Rashidi, A and Azar, E R (2020) Automated methods for activity recognition of construction workers and equipment: State-of-the-art review. Journal of Construction Engineering and Management, 146(6): 0001843, ISSN 0733-9364

Sherafat, Behnam (2022) Acoustical modeling of construction jobsites with multiple operational machines for activity recognition and productivity analysis. PhD thesis, University of Utah, USA.

Shi, G; Lu, D; Liu, Z; Du, X; Zhang, Q; Zhao, L and Wang, Z (2024) Material distribution planning method and experimental verification under multinode and multivehicle scene. Journal of Construction Engineering and Management, 150(11): 04024161, ISSN 0733-9364

Shi, J J (1999) A neural network based system for predicting earthmoving production. Construction Management and Economics, 17(4), pp. 463-471. ISSN 01446193

Shiha, A; Dorra, E M and Nassar, K (2020) Neural networks model for prediction of construction material prices in Egypt using macroeconomic indicators. Journal of Construction Engineering and Management, 146(3): 04020010, ISSN 0733-9364

Shiha, A and El-adaway, I H (2025) Forecasting state-level construction labor earnings for enhanced project cost control: An econometric and deep-learning analysis of the leading economic indicators. Journal of Construction Engineering and Management, 151(12): 04025198, ISSN 0733-9364

Shirazi, D H and Toosi, H (2023) Deep multilayer perceptron neural network for the prediction of Iranian dam project delay risks. Journal of Construction Engineering and Management, 149(4): 04023011, ISSN 0733-9364

Shrestha, K K (2016) Causes of change orders and its impact on road maintenance contracts. PhD thesis, University of Nevada, Las Vegas, USA.

Sirca, G F (2019) Analysis of full-scale in-service civil engineering structures. PhD thesis, Ohio State University, USA.

Sivapragasam, C; Ajith, S and Arumugaprabu, V (2023) A conceptual framework for minimizing construction site accidents using task-personnel nexus matrix. International Journal of Construction Management, 23(9), pp. 1603-1610. ISSN 1562-3599

Slicher, A W R (1997) A strategic decision support system for a consulting engineering firm using a hybrid neural network-expert system approach. PhD thesis, University of Leeds, UK.

Smith, J and Wyatt, R (1998) Criteria for strategic decision-making at the pre-briefing stage. In: Hughes, W (ed.) Proceedings of 14th Annual ARCOM Conference, 9-11 September 1998, Reading, UK.

Soemardi, B W (1993) Fuzzy neural network models for design/construction processes. PhD thesis, University of Kentucky, USA.

Song, L (2004) Productivity modeling for steel fabrication projects. PhD thesis, University of Alberta, Canada.

Song, L and Abourizk, S M (2008) Measuring and modeling labor productivity using historical data. Journal of Construction Engineering and Management, 134(10), pp. 786-794. ISSN 0733-9364

Sonmez, R and Rowings, J E (1998) Construction labor productivity modeling with neural networks. Journal of Construction Engineering and Management, 124(6), pp. 498-504. ISSN 0733-9364

Soutos, M K (2006) Forecasting elemental building cost percentages using regression analysis and neural network techniques. PhD thesis, University of Manchester, UK.

Su, Y; Wang, J; Shou, W; Wu, P; Wu, C and Xu, S (2026) Intentions prediction for human–robot collaboration in utility tunnel maintenance. Engineering, Construction and Architectural Management, 33(15), pp. 1-21. ISSN 0969-9988

Suarez, J J (2004) A neural network model to predict business failure in construction companies, in the United States of America. PhD thesis, University of Florida, USA.

Sun, Y.; Lin, K.; Wang, J.; Zhu, F.; Wang, L. and Lu, L. (2026) Risk assessment of mountain tunnel entrance collapse based on pso-LSTM surface settlement prediction. Engineering, Construction and Architectural Management, 33(3), pp. 2586-2605. ISSN 0969-9988

Sutrisna, M; Tjia, D and Wu, P (2021) Developing a predictive model of construction industry-university research collaboration. Construction Innovation, 21(4), pp. 761-781. ISSN 1471-4175

Syachrani, S (2010) Advanced sewer asset management using dynamic deterioration models. PhD thesis, Oklahoma State University, USA.

Taha, M A-E (1994) Applying distributed artificial intelligence to the prequalification of construction contractors. PhD thesis, University of Wisconsin - Madison, USA.

Taheri, Ali (2024) The framework of an infrastructure performance model based on the concepts of civil integrated management (CIM). PhD thesis, Florida State University, USA.

Talebi, S; Wu, S; Sen, A; Zakizadeh, N; Sun, Q and Lai, J (2025) Infrastructure automated defect detection with machine learning: A systematic review. International Journal of Construction Management, 25(16), pp. 1917-1928. ISSN 1562-3599

Tam, C M; Tong, T K L; Lau, T C T and Chan, K K (2005) Selection of vertical formwork system by probabilistic neural networks models. Construction Management and Economics, 23(3), pp. 245-254. ISSN 01446193

Tam, C M; Tong, T K L and Tse, S L (2002) Artificial neural networks model for predicting excavator productivity. Engineering, Construction and Architectural Management, 9(5-6), pp. 446-452. ISSN 0969-9988

Tam, C M; Tong, T K L and Tse, S L (2004) Modelling hook times of mobile cranes using artificial neural networks. Construction Management and Economics, 22(8), pp. 839-849. ISSN 01446193

Tang, B Q; Han, J; Guo, G F; Chen, Y and Zhang, S (2019) Building material prices forecasting based on least square support vector machine and improved particle swarm optimization. Architectural Engineering and Design Management, 15(3), pp. 196-212. ISSN 1745-2007

Tanratanawong, S (2001) A neural network model to forecast construction output in the United Kingdom. PhD thesis, University of Newcastle upon Tyne, UK.

Tanratanawong, S and Scott, S (2000) A neural network model to forecast national construction output. Journal of Financial Management of Property and Construction, 5(1-2), pp. 65-77. ISSN 1366-4387

Thaesler-Garibaldi, M P (2005) A methodology to develop an integrated engineering system to estimate quantities for bridge repairs at the pre-design stage. PhD thesis, Georgia Institute of Technology, USA.

Thakare, P and Ravi Sankar V (2024) Advanced pest detection strategy using hybrid optimization tuned deep convolutional neural network. Journal of Engineering, Design and Technology, 22(3), pp. 645-678. ISSN 1726-0531

Tian, D; Li, M; Han, S and Shen, Y (2022) A novel and intelligent safety-hazard classification method with syntactic and semantic features for large-scale construction projects. Journal of Construction Engineering and Management, 148(10): 04022109, ISSN 0733-9364

Titirla, M and Aretoulis, G (2019) Neural network models for actual duration of Greek highway projects. Journal of Engineering, Design and Technology, 17(6), pp. 1323-1339. ISSN 1726-0531

Toğan, V; Mostofi, F and Tokdemir, O B (2026) Evaluating the resilience of graph neural network architectures to adversarial and noisy data in high-stakes construction project management. Journal of Construction Engineering and Management, 152(4): 04026029, ISSN 0733-9364

Tsehayae, A A and Fayek, A R (2016) System model for analysing construction labour productivity. Construction Innovation, 16(2), pp. 203-228. ISSN 1471-4175

Tu, J; Liu, Y; Zhou, M and Li, R (2021) Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-bp optimization network. Journal of Engineering, Design and Technology, 19(2), pp. 412-422. ISSN 1726-0531

Tuvayanond, W; Kamchoom, V and Prasittisopin, L (2026) Efficient machine learning for strength prediction of ready-mix concrete production (prolonged mixing). Construction Innovation, 26(2), pp. 369-394. ISSN 1471-4175

Uggla, G (2021) Model and reality: Connecting BIM and the built environment. PhD thesis, KTH Royal Institute of Technology, Sweden.

Umuhoza, E and An, S H (2024) Ann model predicting quality performance for building construction projects in Rwanda. International Journal of Construction Management, 24(15), pp. 1679-1688. ISSN 1562-3599

Utomo, C and Rahmawati, Y (2020) Agreement options for negotiation on material location decision of housing development. Construction Innovation, 20(2), pp. 209-222. ISSN 1471-4175

Van Tol, A A (2005) Agent embedded simulation modeling framework for construction engineering and management applications. PhD thesis, University of Alberta, Canada.

Vojinovic, Z and Kecman, V (2001) Modelling empirical data to support project cost estimating: Neural networks versus traditional methods. Construction Innovation, 1(4), pp. 227-243. ISSN 1471-4175

Wang, H (2021) BIM-based knowledge management for the use of construction and facilities management knowledge in construction projects. PhD thesis, Queen's University Belfast, UK.

Wang, R and Gong, D (2025) Abnormal behavior recognition algorithm in small sample scenarios for a utility tunnel project based on dcgan. Journal of Construction Engineering and Management, 151(6): 04025043, ISSN 0733-9364

Wang, S; Hasan, M and Lu, M (2024) Global sensitivity analysis methodology for construction simulation models: Multiple linear regressions versus multilayer perceptions. Journal of Construction Engineering and Management, 150(5): 04024035, ISSN 0733-9364

Wang, S; Park, S; Kim, J and Kim, J (2025) Safety helmet monitoring on construction sites using yolov10 and advanced transformer architectures with surveillance and body-worn cameras. Journal of Construction Engineering and Management, 151(11): 04025186, ISSN 0733-9364

Wanous, M (2000) A neurofuzzy expert system for competitive tendering in civil engineering. PhD thesis, University of Liverpool, UK.

Wanous, M; Boussabaine, H A and Lewis, J (2003) A neural network bid/no bid model: The case for contractors in Syria. Construction Management and Economics, 21(7), pp. 737-744. ISSN 01446193

Wei, Y (2019) Data-driven approaches for analysis of building energy consumption and indoor occupancy behavior. PhD thesis, University of Nottingham, UK.

Williams, T P (1994) Predicting changes in construction cost indexes using neural networks. Journal of Construction Engineering and Management, 120(2), pp. 306-320. ISSN 0733-9364

Williams, T P (2002) Predicting completed project cost using bidding data. Construction Management and Economics, 20(3), pp. 225-235. ISSN 01446193

Williams, T P (2005) Bidding ratios to predict highway project costs. Engineering, Construction and Architectural Management, 12(1), pp. 38-51. ISSN 0969-9988

Wilmot, C G and Mei, B (2005) Neural network modeling of highway construction costs. Journal of Construction Engineering and Management, 131(7), pp. 765-771. ISSN 0733-9364

Wong, P S P; Cheung, S O and Hardcastle, C (2007) Embodying learning effect in performance prediction. Journal of Construction Engineering and Management, 133(6), pp. 474-482. ISSN 0733-9364

Wong, T; Wei, Y; Zeng, Y; Jie, Y and Zhao, X (2025) A novel real-time torque prediction of epb shield in mixed ground using machine learning method based on geological knowledge fusion. Journal of Construction Engineering and Management, 151(3): 04025005, ISSN 0733-9364

Xia, Y (2020) Research on dynamic data monitoring of steel structure building information using BIM. Journal of Engineering, Design and Technology, 18(5), pp. 1165-1173. ISSN 1726-0531

Xiahou, X; Li, Z; Xia, J; Zhou, Z and Li, Q (2023) A feature-level fusion-based multimodal analysis of recognition and classification of awkward working postures in construction. Journal of Construction Engineering and Management, 149(12): 04023138, ISSN 0733-9364

Xiang, P; Yang, S; Yuan, Y and Li, R (2025) Integrated measurement of public safety risks in international construction projects in the belt and road initiative. Engineering, Construction and Architectural Management, 32(7), pp. 4673-4699. ISSN 0969-9988

Xu, X and Zhang, Y (2024) Office property price index forecasting using neural networks. Journal of Financial Management of Property and Construction, 29(1), pp. 52-82. ISSN 1366-4387

Xu, Y; Shen, X; Lim, S and Li, X (2021) Three-dimensional object detection with deep neural networks for automatic as-built reconstruction. Journal of Construction Engineering and Management, 147(9): 04021098, ISSN 0733-9364

Xu, Yongzhi (2021) Automated and real-time scan-to-BIM through deep learning-based object detection. PhD thesis, University of New South Wales, Australia.

Xue, G; Liu, S; Ren, L and Gong, D (2023) Adaptive cross-scenario few-shot learning framework for structural damage detection in civil infrastructure. Journal of Construction Engineering and Management, 149(5): 04023020, ISSN 0733-9364

Yan, J (2020) 3D printing optimization algorithm based on back-propagation neural network. Journal of Engineering, Design and Technology, 18(5), pp. 1223-1230. ISSN 1726-0531

Yang, G (2009) Evaluation method research and effect analysis of rural road construction on local development. PhD thesis, Harbin Institute of Technology, China.

Yeh, I C (1998) Quantity estimating of building with logarithm-neuron networks. Journal of Construction Engineering and Management, 124(5), pp. 374-380. ISSN 0733-9364

Yıldız, B; Çağdaş, G and Zincir, I (2024) Architectural space classification considering topological and 3D visual spatial relations using machine learning techniques. Building Research & Information, 52(1-2), pp. 68-86. ISSN 0961-3218

Zayed, T M (2001) Assessment of productivity for concrete bored pile construction. PhD thesis, Purdue University, USA.

Zayed, T M and Halpin, D W (2004) Process versus data oriented techniques in pile construction productivity assessment. Journal of Construction Engineering and Management, 130(4), pp. 490-499. ISSN 0733-9364

Zayed, T M and Halpin, D W (2005) Pile construction productivity assessment. Journal of Construction Engineering and Management, 131(6), pp. 705-714. ISSN 0733-9364

Zhai, P; Wang, J and Zhang, L (2023) Extracting worker unsafe behaviors from construction images using image captioning with deep learning-based attention mechanism. Journal of Construction Engineering and Management, 149(2): 04022164, ISSN 0733-9364

Zhang, F (2022) A hybrid structured deep neural network with word2vec for construction accident causes classification. International Journal of Construction Management, 22(6), pp. 1120-1140. ISSN 1562-3599

Zhang, J and Jiang, S (2025) Construction safety decision-making system based on improved integration of case-based reasoning and rule-based reasoning. Journal of Construction Engineering and Management, 151(9): 04025130, ISSN 0733-9364

Zhang, L (2002) Engineering performance improvement based on the integration of genetic algorithms and artificial neural networks. PhD thesis, Purdue University, USA.

Zhang, L (2021) Artificial neural network intelligent technique and multiple nonlinear regression for prediction and optimization of the transmittance of lightpipes and implementation in BIM. PhD thesis, University of Nottingham, UK.

Zhang, Xutong (2025) Physics-guided machine learning for condition assessment of building structures in operational environments. PhD thesis, University of Technology Sydney, Australia.

Zhang, Y; Liu, L; Song, Z; Zhao, Y and He, S (2024) Enhancing tunnel boring machine penetration rate predictions through particle swarm optimization and elman neural networks. Journal of Construction Engineering and Management, 150(9): 04024116, ISSN 0733-9364

Zhang, Y; Minchin, R E; Flood, I and Ries, R J (2023) Preliminary cost estimation of highway projects using statistical learning methods. Journal of Construction Engineering and Management, 149(5): 04023026, ISSN 0733-9364

Zhao, W.; Li, K.; Liu, G.; Chen, B.; Fan, M. and Yin, S. (2026) A three-stage intelligent crack detection method for concrete bridges based on deep neural networks. Journal of Construction Engineering and Management, 152(6): 04026067, ISSN 0733-9364

Zhao, Y; Chen, W; Arashpour, M; Yang, Z; Shao, C and Li, C (2022) Predicting delays in prefabricated projects: Sd-bp neural network to define effects of risk disruption. Engineering, Construction and Architectural Management, 29(4), pp. 1753-1776. ISSN 0969-9988

Zhong, B; Shen, L; Pan, X; Zhong, X and He, W (2024) Dispute classification and analysis: Deep learning-based text mining for construction contract management. Journal of Construction Engineering and Management, 150(1): 04023151, ISSN 0733-9364

Zhou, Y; Liang, H; Foo, Z L; Koh, Y Z and Yeoh, J K W (2026) Audio-based excavator preoperational checks using few-shot deep learning. Journal of Construction Engineering and Management, 152(1): 04025212, ISSN 0733-9364

Zhou, J; Zheng, X; Wang, F and Tian, D (2025) Intelligent identification approach for accident causation in hydraulic and hydropower engineering construction. Journal of Construction Engineering and Management, 151(12): 04025195, ISSN 0733-9364

Zhou, Xiaoyan (2023) A reinforcement learning neural network approach to online optimization of construction manufacturing processes. PhD thesis, University of Florida, USA.

Zhou, Y and Elhag, T (2007) Financial assessment using neural networks. In: Boyd, D (ed.) Proceedings of 23rd Annual ARCOM Conference, 3-5 September 2007, Belfast, UK.

al Nageim, H; Nagar, R and Lisboa, P J G (2007) Comparison of neural network and binary logistic regression methods in conceptual design of tall steel buildings. Construction Innovation, 7(3), pp. 240-253. ISSN 1471-4175

de Silva, N; Ranasinghe, M and de Silva, C R (2013) Use of ANNs in complex risk analysis applications. Built Environment Project and Asset Management, 3(1), pp. 123-140. ISSN 2044-124X