Artificial Neural Network Prediction of Total Construction Cost Using Building Elements for Low- to Mid-Rise Buildings
Abo Yasser L. Manalindo
a
,
Dante L. Silva
b
,
Russell L. Diona
c
,
Kevin Lawrence M. De Jesus
d
a School of Graduate Studies, Mapua University, 658 Muralla St., 1002, Intramuros, Manila, Philippines
b School of Civil, Environmental, and Geological Engineering, Mapua University, 658 Muralla St, 1002, Intramuros, Manila, Philippines
c Department of Information and Technology, University of Technology and Applied Sciences, Al Khuwair, PO Box 74, Muscat, 133, Sultanate of Oman
d Department of Civil Engineering, College of Engineering, FEU Institute of Technology, P. Paredes St., 1015, Sampaloc, Manila, Philippines
Abstract: In recent years, the construction sector in the Philippines has faced significant challenges stemming from various events and occurrences, leading to cost overruns and delays in project timelines. A critical element for every construction undertaking's accomplishment is cost evaluation. Precisely approximating the cost of a project involves thorough consideration of various elements, making it a difficult undertaking to forecast. Several building constructions nowadays produce high cost overrun because of unforeseen change in the project budget that raises the overall project cost such as the complexity of the building system and the organization’s environment. The aim of this paper is to offer a potential prediction for cost estimation, with the goal of minimizing the substantial risk of cost overruns in low- to mid-rise buildings. In this study, the structural elements for low- to mid-rise buildings were utilized from building constructions, such as the number of exterior walls (QEW), type of construction material (TCM), building height (HB), total gross area (TGA), building footprint area (BFA), type of occupancy (TO), number of floors (NF), quantity of shear walls (QSW), and number of columns (NC); an artificial neural network (ANN) model was employed in this research to establish a model for forecasting the total construction cost (TCC). With a correlation value (R) of 0.999890 and a mean absolute percentage error (MAPE) of 0.601%, the modeling results shown that the best model structure was 9-25-1 (input-hidden-output), indicating its effectiveness and efficacy in forecasting the TCC. The impact of each variable employed as an input variable (IV) in the model establishment was seen employing the connection weights (CW) through Garson’s algorithm (GA). The calculation exhibited the order of influence observed as QSW > NC > HB > NF > QEW > TGA > BFA > TO > TCM, wherein the quantity of the shear walls is seen to have the most contribution to the construction cost. Moreover, to check its performance versus other prediction modeling tools, a multiple linear regression (MLR) model was also created and compared to the governing prediction model (GPM). The MAPE of the BP-NN is 7.108 times better than that of the created MLR model.