Items with Index Term: learning algorithm

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

Al-Aomar, R; AlTal, M and Abel, J (2024) A data-driven predictive maintenance model for hospital HVAC system with machine learning. Building Research & Information, 52(1-2), pp. 207-224. ISSN 0961-3218

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

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

Charbel, G; Assaad, R H; Qiao, Y and Labi, S (2025) Predicting the level of competition and determining optimal bidding strategies for bundled projects: Integrating machine-learning algorithms and probabilistic modeling. Journal of Construction Engineering and Management, 151(11): 04025178, ISSN 0733-9364

Cho, C; Kim, K; Park, J and Cho, Y K (2018) Data-driven monitoring system for preventing the collapse of scaffolding structures. Journal of Construction Engineering and Management, 144(8): 04018077, ISSN 0733-9364

Eslamirad, N; Malekpour Kolbadinejad, S; Mahdavinejad, M and Mehranrad, M (2020) Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran. Smart and Sustainable Built Environment, 9(4), pp. 361-374. ISSN 2046-6099

Feng, K; Chen, S; Lu, W; Wang, S; Yang, B; Sun, C and Wang, Y (2023) Embedding ensemble learning into simulation-based optimisation: A learning-based optimisation approach for construction planning. Engineering, Construction and Architectural Management, 30(1), pp. 259-295. ISSN 0969-9988

Ferguson, M (2020) Improving mobile robot navigation with dynamic building information models and stochastic neural control. PhD thesis, Stanford University, USA.

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

Godby, C J (2002) A computational study of lexicalized noun phrases in English. PhD thesis, Ohio State University, USA.

Harode, A; Thabet, W and Leite, F (2024) Formulation of feature and label space using modified Delphi in support of developing a machine-learning algorithm to automate clash resolution. Journal of Construction Engineering and Management, 150(3): 04023173, ISSN 0733-9364

Helaly, H; El-Rayes, K; Ignacio, E J and Joan, H J (2025) Comparison of machine-learning algorithms for estimating cost of conventional and accelerated bridge construction methods during early design phase. Journal of Construction Engineering and Management, 151(3): 04025004, ISSN 0733-9364

Jebelli, H (2019) Wearable biosensors to understand construction workers' mental and physical stress. PhD thesis, University of Michigan, USA.

Jebelli, H; Choi, B and Lee, S H (2019) Application of wearable biosensors to construction sites. I: Assessing workers' stress. Journal of Construction Engineering and Management, 145(12): 04019079, ISSN 0733-9364

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

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.

Lataniotis, C (2019) Data-driven uncertainty quantification for high-dimensional engineering problems. DSc thesis, ETH Zürich, Switzerland.

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

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

McAleenan, P (2020) Moral responsibility and action in the use of artificial intelligence in construction. Proceedings of Institution of Civil Engineers: Management, Procurement and Law, 173(4), pp. 166-174. ISSN 17514304

Mousavi, Milad (2025) Evolving and proactive risk modelling in underground working environments. PhD thesis, University of New South Wales, Australia.

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

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

Sulaimon, I A; Alaka, H; Olu-Ajayi, R; Ahmad, M; Ajayi, S and Hye, A (2024) Effect of traffic data set on various machine-learning algorithms when forecasting air quality. Journal of Engineering, Design and Technology, 22(3), pp. 1030-1056. ISSN 1726-0531

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.

Tantiprabha, P (1990) Acquisition of strategic management concepts from construction project data: An inductive learning approach. PhD thesis, University of Texas at Austin, USA.

Wang, J and Ashuri, B (2017) Predicting ENR construction cost index using machine-learning algorithms. International Journal of Construction Education and Research, 13(1), pp. 47-63. ISSN 1557-8771

Wu, L; Mohamed, E; Jafari, P and Abourizk, S (2023) Machine learning-based Bayesian framework for interval estimate of unsafe-event prediction in construction. Journal of Construction Engineering and Management, 149(11): 04023118, ISSN 0733-9364

Xiang, Z; Rashidi, A and Ou, G (2021) Automated framework to translate rebar spatial information from GPR into BIM. Journal of Construction Engineering and Management, 147(10): 04021120, ISSN 0733-9364

Zeng, C S (2025) Decision under intensity-based costs in large-scale systems. PhD thesis, Princeton University, 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.

Zu, L and Liao, W (2025) Reinforcement learning-based multiobjective and multiconstraint production scheduling for precast concrete. Journal of Construction Engineering and Management, 151(8): 04025089, ISSN 0733-9364