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A platform for research: civil engineering, architecture and urbanism
Prediction of cost and duration of building construction using artificial neural network
https://orcid.org/https://orcid.org/0000-0003-4863-7628
In this research study, the investigation of building details on the construction project cost and duration using artificial neural networks (ANNs) which possesses the ability to generalize complex input–output relationships between given datasets was carried out. From relevant literature review, expert judgment, and extensive field survey, system database were generated with six input factors, namely, activities count (Act.), building area (BA), foundation type (FT), floors number (storey), class of clients and contractors, and two output parameters (duration and cost). The results obtained indicated higher cost and duration variations for the projects given to sole and mini-contractors compared to medium and multi-companies because of inadequate technical advancements and resource personnel to coordinate and manage the construction project activities to prevent cost overrun. The bidding cost and negotiation fees were also observed to effect the choice of contractors’ class recruited for the construction job as the clients with higher financial capacity such as government and cooperate organizations negotiated and hired the multi and medium companies. Feed-forward back-propagation network was used in the smart intelligent modeling development in MATLAB using Levenberg–Marquardt training algorithm and mean squared error (MSE) performance criteria to achieve (6-22-2) optimized network architecture. Using loss-function parameters (mean absolute error, MAE and root mean squared error, MSE and multiple linear regression, MLR) statistical method, the developed ANN-model prediction performance was evaluated. The computed results showed good correlation between ANN model and actual results with average R2 of 0.99995 better than MLR result of 0.6986; also, MAE of 0.2952 and RMSE of 0.5638 were calculated which indicates a robust model.
Prediction of cost and duration of building construction using artificial neural network
https://orcid.org/https://orcid.org/0000-0003-4863-7628
In this research study, the investigation of building details on the construction project cost and duration using artificial neural networks (ANNs) which possesses the ability to generalize complex input–output relationships between given datasets was carried out. From relevant literature review, expert judgment, and extensive field survey, system database were generated with six input factors, namely, activities count (Act.), building area (BA), foundation type (FT), floors number (storey), class of clients and contractors, and two output parameters (duration and cost). The results obtained indicated higher cost and duration variations for the projects given to sole and mini-contractors compared to medium and multi-companies because of inadequate technical advancements and resource personnel to coordinate and manage the construction project activities to prevent cost overrun. The bidding cost and negotiation fees were also observed to effect the choice of contractors’ class recruited for the construction job as the clients with higher financial capacity such as government and cooperate organizations negotiated and hired the multi and medium companies. Feed-forward back-propagation network was used in the smart intelligent modeling development in MATLAB using Levenberg–Marquardt training algorithm and mean squared error (MSE) performance criteria to achieve (6-22-2) optimized network architecture. Using loss-function parameters (mean absolute error, MAE and root mean squared error, MSE and multiple linear regression, MLR) statistical method, the developed ANN-model prediction performance was evaluated. The computed results showed good correlation between ANN model and actual results with average R2 of 0.99995 better than MLR result of 0.6986; also, MAE of 0.2952 and RMSE of 0.5638 were calculated which indicates a robust model.
Prediction of cost and duration of building construction using artificial neural network
https://orcid.org/https://orcid.org/0000-0003-4863-7628
In this research study, the investigation of building details on the construction project cost and duration using artificial neural networks (ANNs) which possesses the ability to generalize complex input–output relationships between given datasets was carried out. From relevant literature review, expert judgment, and extensive field survey, system database were generated with six input factors, namely, activities count (Act.), building area (BA), foundation type (FT), floors number (storey), class of clients and contractors, and two output parameters (duration and cost). The results obtained indicated higher cost and duration variations for the projects given to sole and mini-contractors compared to medium and multi-companies because of inadequate technical advancements and resource personnel to coordinate and manage the construction project activities to prevent cost overrun. The bidding cost and negotiation fees were also observed to effect the choice of contractors’ class recruited for the construction job as the clients with higher financial capacity such as government and cooperate organizations negotiated and hired the multi and medium companies. Feed-forward back-propagation network was used in the smart intelligent modeling development in MATLAB using Levenberg–Marquardt training algorithm and mean squared error (MSE) performance criteria to achieve (6-22-2) optimized network architecture. Using loss-function parameters (mean absolute error, MAE and root mean squared error, MSE and multiple linear regression, MLR) statistical method, the developed ANN-model prediction performance was evaluated. The computed results showed good correlation between ANN model and actual results with average R2 of 0.99995 better than MLR result of 0.6986; also, MAE of 0.2952 and RMSE of 0.5638 were calculated which indicates a robust model.
Prediction of cost and duration of building construction using artificial neural network
Asian J Civ Eng
Ujong, Jesam Abam (author) / Mbadike, Elvis Michael (author) / Alaneme, George Uwadiegwu (author)
Asian Journal of Civil Engineering ; 23 ; 1117-1139
2022-11-01
23 pages
Article (Journal)
Electronic Resource
English
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