Items with Index Term: back propagation

Deng, S; Ni, P; Zhu, H; Cai, Y and Pan, Y (2024) Artificial cognition to predict and explain the potential unsafe behaviors of construction workers. Journal of Construction Engineering and Management, 150(7): 04024074, ISSN 0733-9364

Doğan, S Z (2005) Using machine learning techniques for early cost prediction of structural systems of buildings. PhD thesis, Izmir Institute of Technology, Turkey.

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-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

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

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

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

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

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.

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

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.

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

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

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

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

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

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

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

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

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

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.