Items with Index Term: artificial neural network

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

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

Abourizk, S; Knowles, P and Hermann, U R (2001) Estimating labor production rates for industrial construction activities. Journal of Construction Engineering and Management, 127(6), pp. 502-511. ISSN 0733-9364

Abu-Mahfouz, E; Al-Dahidi, S; Gharaibeh, E and Alahmer, A (2025) A novel feature engineering-based hybrid approach for precise construction cost estimation using fuzzy-AHP and artificial neural networks. International Journal of Construction Management, 25(15), pp. 1800-1810. ISSN 1562-3599

Aghajamali, K; Metvaei, S; Suliman, A; Lei, Z and Chen, Q (2025) Development of a prefabricated construction productivity estimation model through BIM and data augmentation processes. Construction Management and Economics, 43(5), pp. 340-359. ISSN 0144-6193

Aghimien, D; Aigbavboa, C O; Chan, D W M and Aghimien, E I (2024) Determinants of cloud computing deployment in South African construction organisations using structural equation modelling and machine learning technique. Engineering, Construction and Architectural Management, 31(3), pp. 1037-1060. ISSN 0969-9988

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 and Smith, S D (2014) Dealing with construction cost overruns using data mining. Construction Management and Economics, 32(7-8), pp. 682-694. ISSN 01446193

Ahiaga-Dagbui D D, S S D (2013) My cost runneth over: Data mining to reduce construction cost overruns. In: Smith, S D and Ahiaga-Dagbui, D D (eds.) Proceedings of 29th Annual ARCOM Conference, 2-4 September 2013, Reading, 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.

Ahmed, M O; Khalef, R; Ali, G G and El-Adaway, I H (2021) Evaluating deterioration of tunnels using computational machine learning algorithms. Journal of Construction Engineering and Management, 147(10): 04021125, ISSN 0733-9364

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

Al Jassmi, H; Al Ahmad, M and Ahmed, S (2021) Automatic recognition of labor activity: A machine learning approach to capture activity physiological patterns using wearable sensors. Construction Innovation, 21(4), pp. 555-575. ISSN 1471-4175

Al Yami, H M A (2017) Decision making analysis for an integrated risk management framework of maritime container port infrastructure and transportation systems. PhD thesis, Liverpool John Moores University, UK.

Al-Sobiei, O S; Arditi, D and Polat, G (2005) Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques. Construction Management and Economics, 23(4), pp. 423-430. ISSN 01446193

Al-Sobiei, O S; Arditi, D and Polat, G (2005) Managing owner's risk of contractor default. Journal of Construction Engineering and Management, 131(9), pp. 973-978. ISSN 0733-9364

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-Tawal, D R; Arafah, M and Sweis, G J (2021) A model utilizing the artificial neural network in cost estimation of construction projects in Jordan. Engineering, Construction and Architectural Management, 28(9), pp. 2466-2488. 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

Alharbi, E A and Alsehaimi, A (2025) Machine learning model to predict the cooling load of mosque buildings during the design stage. International Journal of Construction Management, 25(8), pp. 965-979. ISSN 1562-3599

Aligamhe, V I; Mustapa, M; Ogbu, C P and Misnan, M S (2024) Modelling cost-risk impact on Nigerian highway projects using multiple linear regression and artificial neural networks. Journal of Construction in Developing Countries, 29(2), pp. 107-128. ISSN 1823-6499

Alnaqbi, A; Al-Khateeb, G G and Zeiada, W (2025) Machine learning applications for predicting longitudinal cracking in continuously reinforced concrete pavement. Construction Economics and Building, 25(1), pp. 143-170. ISSN 2204-9029

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

Alqahtani, A and Whyte, A (2016) Estimation of life-cycle costs of buildings: Regression vs artificial neural network. Built Environment Project and Asset Management, 6(1), pp. 30-43. ISSN 2044-124X

Alqershy, M T and Kishore, R (2023) Construction claims prediction using ann models: A case study of the Indian construction industry. International Journal of Construction Management, 23(6), pp. 1097-1108. ISSN 1562-3599

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

Alzahrani, H; Arif, M; Kaushik, A; Goulding, J and Heesom, D (2021) Artificial neural network analysis of teachers' performance against thermal comfort. International Journal of Building Pathology and Adaptation, 39(1), pp. 20-32. ISSN 23984708

Alzahrani, H; Arif, M; Kaushik, A K; Rana, M Q and Aburas, H M (2023) Evaluating the effects of indoor air quality on teacher performance using artificial neural network. Journal of Engineering, Design and Technology, 21(2), pp. 604-618. ISSN 1726-0531

Anbar, D R; Chang, T; Deng, X and Mahmoud, M R I (2024) Implementing effective knowledge management in international construction projects by eliminating knowledge hiding. Journal of Construction Engineering and Management, 150(8): 04024082, ISSN 0733-9364

Arida, M A (2024) Enhancing thermal comfort in commercial buildings: Contributions of machine learning algorithms for optimizing predictive thermal comfort indices. PhD thesis, North Carolina Agricultural and Technical State University, USA.

Arora, M; Prakash, A; Mittal, A and Singh, S (2024) Examining the slow acceptance of HR analytics in the Indian engineering and construction industry: A SEM-ann-based approach. Engineering, Construction and Architectural Management, 31(5), pp. 1973-1993. ISSN 0969-9988

Assaad, R H; Omran, A; Soliman, N and Assaf, G (2024) Prediction of the lateral pressure of self-consolidating concrete on construction formwork systems using machine-learning algorithms. Journal of Construction Engineering and Management, 150(9): 04024110, ISSN 0733-9364

Atapattu, C N; Domingo, N and Sutrisna, M (2025) A bibliometric review of the statistical modelling techniques for cost estimation of infrastructure projects. Smart and Sustainable Built Environment, 14(5), pp. 1369-1388. ISSN 2046-6099

Attalla, M and Hegazy, T (2003) Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression. Journal of Construction Engineering and Management, 129(4), pp. 405-411. 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 L S (2012) Intelligent contractor default prediction model for surety bonding in the construction industry. PhD thesis, University of Alberta, Canada.

Awuku, B; Asa, E; Baffoe-Twum, E and Essegbey, A (2024) Conceptual cost estimation of highway bid items: a systematic literature review. Engineering, Construction and Architectural Management, 31(3), pp. 1187-1221. ISSN 0969-9988

Ayat, M; Ullah, M; Pervez, Z; Lawrence, J; Kang, C W and Ullah, A (2025) Analyzing the differential impact of variables on the success of solicited and unsolicited private participation in infrastructure projects using machine learning techniques. Engineering, Construction and Architectural Management, 32(12), pp. 7909-7937. ISSN 0969-9988

Ayeni, Reuben Ebenmosi (2025) Intelligent project duration estimating system: An AI-based predictive approach for construction time estimation. PhD thesis, London South Bank University, UK.

Ayhan, B U and Tokdemir, O B (2020) Accident analysis for construction safety using latent class clustering and artificial neural networks. Journal of Construction Engineering and Management, 146(3): 04019114, ISSN 0733-9364

Ayinla, K; Saka, A; Seidu, R and Madanayake, U (2023) The impact of artificial intelligence on construction costing practice. In: Tutesigensi, A and Neilson, C J (eds.) Proceedings of 39th Annual ARCOM Conference, 4-6 September 2023, University of Leeds, Leeds, UK.

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

Badran, Y (2020) Analysis of construction and stakeholder risks for Public Private Partnership projects in developing countries: a comparative analysis using Artificial Neural Networks to determine the effect of poor stakeholder management and construction risks on the project's schedule (PPP vs. traditional projects). PhD thesis, University of Salford, UK.

Bai, L; Wang, Z; Wang, H; Huang, N and Shi, H (2021) Prediction of multiproject resource conflict risk via an artificial neural network. Engineering, Construction and Architectural Management, 28(10), pp. 2857-2883. ISSN 0969-9988

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.

Ballal, T M A (1999) The use of artificial neural networks for modelling buildability in preliminary structural design. PhD thesis, Loughborough University, UK.

Ballal, T M A and Sher, W D (2003) Artificial neural network for the selection of buildable structural systems. Engineering, Construction and Architectural Management, 10(4), pp. 263-271. ISSN 0969-9988

Bangaru, S S; Wang, C; Zhou, X; Jeon, H W and Li, Y (2020) Gesture recognition-based smart training assistant system for construction worker earplug-wearing training. Journal of Construction Engineering and Management, 146(12): 04020144, ISSN 0733-9364

Barati, K; Shen, X; Li, N and Carmichael, D G (2022) Automatic mass estimation of construction vehicles by modeling operational and engine data. Journal of Construction Engineering and Management, 148(3): 4021208, ISSN 0733-9364

Barati, Khalegh (2018) Modeling fuel use, emissions and mass of on-road construction equipment through monitoring field operations. PhD thesis, University of New South Wales, Australia.

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

Bhokha, S and Ogunlana, S O (1999) Application of artificial neural network to forecast construction duration of buildings at the pre-design stage. Engineering, Construction and Architectural Management, 6(2), pp. 133-144. ISSN 0969-9988

Bienvenido-Huertas, D; Rubio-Bellido, C; Sánchez-García, D and Moyano, J (2019) Internal surface condensation risk in façades of Spanish social dwellings. Building Research & Information, 47(8), pp. 928-947. ISSN 0961-3218

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

Chan, A P C; Yang, Y; Wong, F K W; Chan, D W M and Lam, E W M (2015) Wearing comfort of two construction work uniforms. Construction Innovation, 15(4), pp. 473-492. ISSN 1471-4175

Chan, T-W C (2007) Improving the estimation of project overheads in construction companies in Hong Kong. PhD thesis, Loughborough University, UK.

Chanda, E K and Gardiner, S (2010) A comparative study of truck cycle time prediction methods in open-pit mining. Engineering, Construction and Architectural Management, 17(5), pp. 446-460. ISSN 0969-9988

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, W (2019) Integration of building information modeling and internet of things for facility maintenance management. PhD thesis, Hong Kong University of Science and Technology, Hong Kong.

Chen, X; Chang-Richards, A; Ling, F Y Y; Yiu, K T W; Pelosi, A and Yang, N (2024) Effects of digital readiness on digital competence of AEC companies: A dual-stage PLS-SEM-ANN analysis. Building Research & Information, 52(8), pp. 905-922. ISSN 0961-3218

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

Cheung, S-O (1998) Project dispute resolution satisfaction of construction clients in Hong Kong. PhD thesis, University of Wolverhampton, UK.

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 (2010) Bid mark-up selection using artificial neural networks and an entropy metric. Engineering, Construction and Architectural Management, 17(4), pp. 424-439. ISSN 0969-9988

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

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

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

Deepa, G; Niranjana, A J and Balu, A S (2025) A hybrid machine learning approach for early cost estimation of pile foundations. Journal of Engineering, Design and Technology, 23(1), pp. 306-322. ISSN 1726-0531

Delgado, J M D; Oyedele, L; Bilal, M; Ajayi, A; Akanbi, L and Akinade, O (2020) Big data analytics system for costing power transmission projects. Journal of Construction Engineering and Management, 146(1): 05019017, 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; 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

Dissanayaka, S M and Kumaraswamy, M M (1999) Evaluation of factors affecting time and cost performance in Hong Kong building projects. Engineering, Construction and Architectural Management, 6(3), pp. 287-298. ISSN 0969-9988

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 (1999) A methodology for predicting the total average hourly maintenance cost of tracked hydraulic excavators operating in the UK opencast mining industry. PhD thesis, University of Wolverhampton, UK.

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

Edwards, D J; Yang, J; Wright, B C and Love, P E D (2007) Establishing the link between plant operator performance and personal motivation. Journal of Engineering, Design and Technology, 5(2), pp. 173-187. ISSN 1726-0531

El-Gohary, K M; Aziz, R F and Abdel-Khalek, H A (2017) Engineering approach using ANN to improve and predict construction labor productivity under different influences. Journal of Construction Engineering and Management, 143(8): 04017045, ISSN 0733-9364

El-Kholy, A M; Tahwia, A M and Elsayed, M M (2022) Prediction of simulated cost contingency for steel reinforcement in building projects: ANN versus regression-based models. International Journal of Construction Management, 22(9), pp. 1675-1689. ISSN 1562-3599

ElAlem, M A; Mahdi, I M; Mohamadien, H A and Hosny, S (2025) Forecasting scope creep in Egyptian construction projects: An evaluation using artificial neural network (ANN) and random forest models. International Journal of Construction Management, 25(16), pp. 2082-2101. ISSN 1562-3599

Elazouni, A and Salem, O A (2011) Progress monitoring of construction projects using pattern recognition techniques. Construction Management and Economics, 29(4), pp. 355-370. ISSN 1466433X

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 (1998) An artificial neural system for cost estimation of construction projects. In: Hughes, W (ed.) Proceedings of 14th Annual ARCOM Conference, 9-11 September 1998, Reading, 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

Elmousalami, H H (2020) Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review. Journal of Construction Engineering and Management, 146(1): 03119008, ISSN 0733-9364

Elmousalami, H H; Elyamany, A H and Ibrahim, A H (2018) Predicting conceptual cost for field canal improvement projects. Journal of Construction Engineering and Management, 144(11): 04018102, ISSN 0733-9364

Erpen, Mauro Luiz (2020) Analysis of the perception of relative importance of critical success factors in the civil construction industry using artificial neural networks. PhD thesis, Universidade de Brasília, Brazil.

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, S L; Yeh, I C and Chi, W S (2021) Improvement in estimating durations for building projects using artificial neural network and sensitivity analysis. Journal of Construction Engineering and Management, 147(7): 04021050, ISSN 0733-9364

Farooq, M. and Paracha, A. T. (2026) Cost prediction of road construction projects in Pakistan using machine learning. International Journal of Construction Management, 26(8), pp. 1442-1470. ISSN 1562-3599

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

Francis, A and Thomas, A (2022) A machine learning-based life cycle assessment prediction model for the environmental impacts of buildings. In: Tutesigensi, A and Neilson, C J (eds.) Proceedings of 38th Annual ARCOM Conference, 5-7 September 2022, Glasgow Caledonian University, Glasgow, UK.

Gaber, B; Zhan, C; Han, X; Omar, M and Li, G (2024) Employing ANN for daylight and energy prediction of hot climate office buildings: A case study of new cairo, Egypt. Architectural Engineering and Design Management, 20(6), pp. 1752-1776. ISSN 1745-2007

Gardner, B J; Gransberg, D D and Jeong, H D (2016) Reducing data-collection efforts for conceptual cost estimating at a highway agency. Journal of Construction Engineering and Management, 142(11): 04016057, ISSN 0733-9364

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

Gharehbaghi, K and McManus, K (2019) TIS condition monitoring using ANN integration: an overview. Journal of Engineering, Design and Technology, 17(1), pp. 204-217. ISSN 1726-0531

Goh, B H (1997) Construction demand modelling: a systematic approach to using economic indicators and a comparative study of alternative forecasting approaches. PhD thesis, University College London, UK.

Goh, B H (1998) Forecasting residential construction demand in Singapore: A comparative study of the accuracy of time series, regression and artificial neural network techniques. Engineering, Construction and Architectural Management, 5(3), pp. 261-275. ISSN 0969-9988

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

Golizadeh, H; Banihashemi, S; Sadeghifam, A N and Preece, C (2017) Automated estimation of completion time for dam projects. International Journal of Construction Management, 17(3), pp. 197-209. ISSN 1562-3599

Golizadeh, H and Namini, S B (2015) Predicting the significant characteristics of concrete containing palm oil fuel ash. Journal of Construction in Developing Countries, 20(1), pp. 85-98. ISSN 1823-6499

Goodarzizad, P; Mohammadi Golafshani, E and Arashpour, M (2023) Predicting the construction labour productivity using artificial neural network and grasshopper optimisation algorithm. International Journal of Construction Management, 23(5), pp. 763-779. ISSN 1562-3599

Guerra, B C; Koo, H J; Caldas, C and Leite, F (2024) Prediction of waste diversion and identification of trends in construction and demolition waste data using data mining. International Journal of Construction Management, 24(4), pp. 374-383. ISSN 1562-3599

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.

Hamida, A; Alsudairi, A; Alshaibani, K and Alshamrani, O (2021) Environmental impacts cost assessment model of residential building using an artificial neural network. Engineering, Construction and Architectural Management, 28(10), pp. 3190-3215. ISSN 0969-9988

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.

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

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

Heydari Torkamani, M; Shahbazi, Y and Belali Oskoyi, A (2024) Explaining resilience model of historical bazaars using artificial neural network. Smart and Sustainable Built Environment, 13(6), pp. 1538-1559. ISSN 2046-6099

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

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

Hong, S M; Paterson, G; Mumovic, D and Steadman, P (2014) Improved benchmarking comparability for energy consumption in schools. Building Research & Information, 42(1), pp. 47-61. ISSN 0961-3218

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.

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.

Hua, G B (1996) Residential construction demand forecasting using economic indicators: A comparative study of artificial neural networks and multiple regression. Construction Management and Economics, 14(1), pp. 25-34. ISSN 01446193

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

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

Iroham, C O; Misra, S; Emebo, O C and Okagbue, H I (2023) Predictive rental values model for low-income earners in slums: The case of Ijora, Nigeria. International Journal of Construction Management, 23(8), pp. 1426-1435. ISSN 1562-3599

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

Jacques de Sousa, L; Poças Martins, J; Sanhudo, L and Santos Baptista, J (2024) Automation of text document classification in the budgeting phase of the construction process: A systematic literature review. Construction Innovation, 24(7), pp. 292-318. ISSN 1471-4175

Jafarzadeh, R; Ingham, J M; Wilkinson, S; González, V and Aghakouchak, A A (2014) Application of artificial neural network methodology for predicting seismic retrofit construction costs. Journal of Construction Engineering and Management, 140(2): 04013044, 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

Jin, X H (2011) Model for efficient risk allocation in privately financed public infrastructure projects using neuro-fuzzy techniques. Journal of Construction Engineering and Management, 137(11), pp. 1003-1014. 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

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

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 (1998) A hybrid of executive management decision support tools. In: Hughes, W (ed.) Proceedings of 14th Annual ARCOM Conference, 9-11 September 1998, Reading, UK.

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

Khosrowshahi, F (2015) Enhanced project brief: Structured approach to client-designer interface. Engineering, Construction and Architectural Management, 22(5), pp. 474-492. ISSN 0969-9988

Kim, B; Lee, H; Park, H and Kim, H (2012) Framework for estimating greenhouse gas emissions due to asphalt pavement construction. Journal of Construction Engineering and Management, 138(11), pp. 1312-1321. ISSN 0733-9364

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

Kittinaraporn, W; Tuprakay, S and Prasittisopin, L (2022) Effective modeling for construction activities of recycled aggregate concrete using artificial neural network. Journal of Construction Engineering and Management, 148(3): 04021206, ISSN 0733-9364

Koc, K; Ekmekcioğlu, Ö and Gurgun, A P (2023) Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods. Engineering, Construction and Architectural Management, 30(9), pp. 4486-4517. ISSN 0969-9988

Koo, H. J.; Guerra, B. C.; Saka, S. and Leite, F. (2026) Streamlining BIM coordination and clash resolution through risk and relevance analysis. Journal of Construction Engineering and Management, 152(7): 04026092, ISSN 0733-9364

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 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

Lawal, H S; Ahmadu, H A; Abdullahi, M; Yamusa, M A and Abdulrazaq, M (2023) Modeling duration of building renovation projects. Journal of Financial Management of Property and Construction, 28(3), pp. 423-438. ISSN 1366-4387

Lee, J G; Lee, H S; Park, M and Seo, J (2022) Early-stage cost estimation model for power generation project with limited historical data. Engineering, Construction and Architectural Management, 29(7), pp. 2599-2614. ISSN 0969-9988

Lee, Jaeho (2007) A methodology for developing bridge condition rating models based on limited inspection records. PhD thesis, Griffith University, Australia.

Lhee, S C; Issa, R R A and Flood, I (2012) Prediction of financial contingency for asphalt resurfacing projects using artificial neural networks. Journal of Construction Engineering and Management, 138(1), pp. 22-30. ISSN 0733-9364

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 and Love, P E D (1997) Combining rule-based expert systems and artificial neural networks for mark-up estimation. Construction Management and Economics, 17(2), pp. 169-176. ISSN 01446193

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

Lim, T K; Park, S M; Lee, H C and Lee, D E (2016) Artificial neural network-based slip-trip classifier using smart sensor for construction workplace. Journal of Construction Engineering and Management, 142(2): 04015065, ISSN 0733-9364

Liu, Y; Junjia, Y; Wang, H and Alias, A H (2026) Bridging robotics implementation and operational efficiency via agile management: Evidence from commercial residential projects. Journal of Construction Engineering and Management, 152(4): 04026019, ISSN 0733-9364

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

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

Ma, M; Tam, V W Y; Le, K N and Osei-Kyei, R (2024) A systematic literature review on price forecasting models in construction industry. International Journal of Construction Management, 24(11), pp. 1191-1200. ISSN 1562-3599

Malek, M S and Bhatt, V (2024) Investigating the effect of risk reduction strategies on the construction of mega infrastructure project (MIP) success: a SEM-ANN approach. Engineering, Construction and Architectural Management, 31(9), pp. 3575-3598. ISSN 0969-9988

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

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

Marinelli, M D L F N L S (2015) Non-parametric bill-of-quantities estimation of concrete road bridge superstructure: An artificial neural networks approach. In: Raiden, A and Aboagye-Nimo, E (eds.) Proceedings of 31st Annual ARCOM Conference, 7-9 September 2015, Lincoln, UK.

Matel, E; Vahdatikhaki, F; Hosseinyalamdary, S; Evers, T and Voordijk, H (2022) An artificial neural network approach for cost estimation of engineering services. International Journal of Construction Management, 22(7), pp. 1274-1287. ISSN 1562-3599

Mater, Y; Kamel, M; Karam, A and Bakhoum, E (2023) Ann-python prediction model for the compressive strength of green concrete. Construction Innovation, 23(2), pp. 340-359. ISSN 1471-4175

McCluskey, W; Davis, P; Haran, M; McCord, M and McIlhatton, D (2012) The potential of artificial neural networks in mass appraisal: The case revisited. Journal of Financial Management of Property and Construction, 17(3), pp. 274-292. ISSN 1366-4387

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, J; Yan, J; Xue, B; Fu, J and He, N (2018) Reducing construction material cost by optimizing buy-in decision that accounts the flexibility of non-critical activities. Engineering, Construction and Architectural Management, 25(8), pp. 1092-1108. ISSN 0969-9988

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

Mhady, A A; Gürgün, A P; Budayan, C and Koc, K (2025) New hybrid models integrating the firefly optimization algorithm with the artificial neural networks and adaptive neuro-fuzzy inference systems to improve estimation at completion. Journal of Construction Engineering and Management, 151(11): 04025160, ISSN 0733-9364

Mishra, S P; Parbat, D K and Modak, J P (2014) Field data-based mathematical simulation of manual rebar cutting. Journal of Construction in Developing Countries, 19(1), pp. 111-126. ISSN 1823-6499

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

Mohamedein, S. M. and Nassar, A. H. (2026) Calculating cost contingency for residential construction projects in Egypt using a machine learning algorithm. International Journal of Construction Management, 26(9), pp. 1761-1776. ISSN 1562-3599

Mohammed, A; Bahatheq, A; Ghaithan, A; Alshibani, A; Mazher, K M and Alrashidi, A (2026) Predicting schedule delays of construction projects in the oil and gas industry: Comparative study. Built Environment Project and Asset Management, 16(2), pp. 286-303. ISSN 2044-124X

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

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

Nafe Assafi, M.; Wang, J.; Ma, J. and Cotton, J. (2026) Assessing the impact of eye-tracking features on construction hazard identification skills using virtual reality and machine learning. Construction Innovation, 26(5), pp. 1614-1635. ISSN 1471-4175

Nani, G; Mensah, I and Adjei-Kumi, T (2017) Duration estimation model for bridge construction projects in Ghana. Journal of Engineering, Design and Technology, 15(6), pp. 754-777.

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.

Obasi, S N N; Pemberton, J and Awe, O O (2025) A comparative study of soil classification machine learning models for construction management. International Journal of Construction Management, 25(5), pp. 584-593. ISSN 1562-3599

Odeyinka, H A (2003) The development and validation of models for assessing risk impacts on construction cash flow forecast. PhD thesis, Glasgow Caledonian University, UK.

Odeyinka, H A; Lowe, J and Kaka, A P (2013) Artificial neural network cost flow risk assessment model. Construction Management and Economics, 31(5), pp. 423-439. ISSN 01446193

Oduyemi, O; Okoroh, M and Dean, A (2015) Developing an artificial neural network model for life cycle costing in buildings. In: Raiden, A and Aboagye-Nimo, E (eds.) Proceedings of 31st Annual ARCOM Conference, 7-9 September 2015, Lincoln, 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

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

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

Padala, S P S and Goyal, A (2025) Early stage cost prediction model for Indian building construction projects using artificial neural networks. Journal of Financial Management of Property and Construction, 30(3), pp. 377-397. ISSN 1366-4387

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

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 V; Balakrishna Rao, K and Nayak, G (2023) Prediction of recycled coarse aggregate concrete mechanical properties using multiple linear regression and artificial neural network. Journal of Engineering, Design and Technology, 21(6), pp. 1690-1709. ISSN 1726-0531

Pereira, E; Ali, M; Wu, L and Abourizk, S (2020) Distributed simulation-based analytics approach for enhancing safety management systems in industrial construction. Journal of Construction Engineering and Management, 146(1): 04019091, ISSN 0733-9364

Pewdum, W; Rujirayanyong, T and Sooksatra, V (2009) Forecasting final budget and duration of highway construction projects. Engineering, Construction and Architectural Management, 16(6), pp. 544-557. ISSN 0969-9988

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

Poh, Y P (2005) Knowledge-based integrated project duration-cost risk simulation model. PhD thesis, London South Bank University, UK.

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

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

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

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

Said, H M and Kandimalla, P (2018) Performance measurement of building sheet-metal ductwork prefabrication under batch production settings. Journal of Construction Engineering and Management, 144(2): 4017107, ISSN 0733-9364

Salarian, A A; Etemadfard, H; Rahimzadegan, A and Ghalehnovi, M (2023) Investigating the role of clustering in construction-accident severity prediction using a heterogeneous and imbalanced data set. Journal of Construction Engineering and Management, 149(2): 04022161, ISSN 0733-9364

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

Sanyal, A P and Bhattacharya, S P (2024) A comparative analysis between CBR based prediction models and mra models for high-rise construction delay prediction. International Journal of Construction Management, 24(2), pp. 124-136. ISSN 1562-3599

Sarihi, M; Shahhosseini, V and Banki, M T (2023) Development and comparative analysis of the fuzzy inference system-based construction labor productivity models. International Journal of Construction Management, 23(3), pp. 423-433. ISSN 1562-3599

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

Seo, W; Kim, B; Bang, S and Kang, Y (2024) Identifying key financial variables predicting the financial performance of construction companies. Journal of Construction Engineering and Management, 150(3): 04024007, ISSN 0733-9364

Sharma, N; Sood, R and Laishram, B (2025) A hybrid PLS-SEM-ANN approach to COQ optimization. Construction Management and Economics, 43(11), pp. 938-960. ISSN 0144-6193

Shayboun, M; Kifokeris, D and Koch, C (2020) Machine learning for analysis of occupational accidents registration data. In: Scott, L and Neilson, C J (eds.) Proceedings of 36th Annual ARCOM Conference, 7-8 September 2020, Online Event, UK.

Shehab, T; Blampied, N; Nasr, E and Sindhu, L (2024) Ann-based estimation model for the preconstruction cost of pavement rehabilitation projects. International Journal of Construction Management, 24(8), pp. 894-901. ISSN 1562-3599

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

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

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

Silva, A; Dias, J L; Gaspar, P L and De Brito, J (2011) Service life prediction models for exterior stone cladding. Building Research & Information, 39(6), pp. 637-653. ISSN 0961-3218

Singh Rajput, T and Thomas, A (2023) Optimizing passive design strategies for energy efficient buildings using hybrid artificial neural network (ann) and multi-objective evolutionary algorithm through a case study approach. International Journal of Construction Management, 23(13), pp. 2320-2332. ISSN 1562-3599

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.

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

Soetanto, R (2002) Modelling satisfaction for main participants of the construction project coalition: a study of mutual performance assessment. PhD thesis, University of Wolverhampton, UK.

Soetanto, R and Proverbs, D G (2001) Modelling client satisfaction levels: A comparison of multiple regression and artificial neural network techniques. In: Akintoye, A (ed.) Proceedings of 17th Annual ARCOM Conference, 5-7 September 2001, Salford, UK.

Soetanto, R and Proverbs, D G (2004) Intelligent models for predicting levels of client satisfaction. Journal of Construction Research, 5(2), pp. 233-253. ISSN 1609-9451

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

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

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

Tam, C M and Tong, T K L (2003) GA-ANN model for optimizing the locations of tower crane and supply points for high-rise public housing construction. Construction Management and Economics, 21(3), pp. 257-266. ISSN 01446193

Tam, C M and Tong, T K L (2005) Multiple gmdh models for estimating resource requirements. Construction Innovation, 5(2), pp. 115-131. ISSN 1471-4175

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

Tatari, A (2023) Simulating cost risks for prefabricated construction in developing countries using Bayesian networks. Journal of Construction Engineering and Management, 149(6): 04023037, ISSN 0733-9364

Thach, H. P.; Nguyen, L. D. and Nguyen, V. T. (2026) Integrating coastal segmentation on predictive modeling for data-driven bridge asset management. International Journal of Construction Management, 26(10), pp. 2228-2244. ISSN 1562-3599

Tiruneh, G G and Fayek, A R (2022) Hybrid GA-manfis model for organizational competencies and performance in construction. Journal of Construction Engineering and Management, 148(4): 04022002, ISSN 0733-9364

Tiruneh, Getaneh Gezahegne (2021) Hybrid neuro-fuzzy model for construction organizational competencies and performance. PhD thesis, University of Alberta, Canada.

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

Trinh, Hoang T (2021) Optimisation framework for sustainable design of concrete buildings. PhD thesis, Griffith University, Australia.

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

Upadhyaya, Deep Shaileshkumar (2024) Investigation on the adequacy of health and safety management in Indian construction sector. PhD thesis, Gujarat Technological University, India.

Wang, D; Arditi, D and Damci, A (2017) Construction project managers' motivators and human values. Journal of Construction Engineering and Management, 143(4): 04016115, ISSN 0733-9364

Wang, Y; Zuo, J; Pan, M; Tu, B; Chang, R D; Liu, S; Xiong, F and Dong, N (2024) Cost prediction of building projects using the novel hybrid RA-ANN model. Engineering, Construction and Architectural Management, 31(6), pp. 2563-2582. ISSN 0969-9988

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.

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

Wusu, G E; Alaka, H; Yusuf, W; Mporas, I; Toriola-Coker, L and Oseghale, R (2024) A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria. Smart and Sustainable Built Environment, 13(6), pp. 1408-1433. ISSN 2046-6099

Xiong, B; Newton, S; Li, V; Skitmore, M and Xia, B (2019) Hybrid approach to reducing estimating overfitting and collinearity. Engineering, Construction and Architectural Management, 26(10), pp. 2170-2185. ISSN 0969-9988

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

Yousefi, S; Hegazy, T; Capuru̧o, R A C and Attalla, M (2008) System of multiple anns for online planning of numerous building improvements. Journal of Construction Engineering and Management, 134(5), pp. 342-351. ISSN 0733-9364

Yuan, F; Tang, M and Hong, J (2020) Efficiency estimation and reduction potential of the Chinese construction industry via SE-DEA and artificial neural network. Engineering, Construction and Architectural Management, 27(7), pp. 1533-1552. ISSN 0969-9988

Yun, S and Caldas, C H (2009) Analysing decision variables that influence preliminary feasibility studies using data mining techniques. Construction Management and Economics, 27(1), pp. 73-87. ISSN 1466433X

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

Zayed, T M; Halpin, D W and Basha, I M (2005) Productivity and delays assessment for concrete batch plant-truck mixer operations. Construction Management and Economics, 23(8), pp. 839-850. ISSN 01446193

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, M; Cao, T and Zhao, X (2019) Using smartphones to detect and identify construction workers' near-miss falls based on ann. Journal of Construction Engineering and Management, 145(1): 04018120, ISSN 0733-9364

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

al-Attar, H; Sweis, G; Tarawneh, B; Abu-Khader, W; Haddad, L and Sweis, R (2025) Enhanced construction project duration estimation using artificial neural networks: Initial design and planning stages. Construction Economics and Building, 25(2), pp. 168-191. ISSN 2204-9029

d'Apolito, L and Hong, H (2020) Forklift truck performance simulation and fuel consumption estimation. Journal of Engineering, Design and Technology, 18(3), pp. 689-703. ISSN 1726-0531

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