TY - JOUR
T1 - Predicting the construction labour productivity using artificial neural network and grasshopper optimisation algorithm
AU - Goodarzizad, Payam
AU - Mohammadi Golafshani, Emadaldin
AU - Arashpour, Mehrdad
N1 - Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2023
Y1 - 2023
N2 - The construction industry is mainly dependent on human resources, and labour costs are significant. Although many researchers have investigated construction labour productivity (CLP), the lack of adequate studies in this field is evident in developing countries. This paper intends to measure the CLP of the concrete pouring operations related to the construction of commercial-office complex projects in Iran. For this purpose, 19 critical factors with significant impact on the CLP were identified and listed in five groups, including individual, managerial, economic, technical, and environmental aspects. Then, a hybrid model based on artificial neural network (ANN) and Grasshopper optimisation algorithm (GOA) was developed to determine the most influential factors and increase the CLP model’s precision. Data related to the CLP of 24 under-construction commercial-office complex projects in Iran were gathered. Results reveal the most influencing factors on the CLP are labour experience and skill and motivation of labour from the individual group, the amount of pay from the economic group, site accidents from the technical group, proper supervision from the management group, and weather conditions from the environmental group. The findings can facilitate the development of more efficient project schedules, increasing the CLP, and reducing project costs.
AB - The construction industry is mainly dependent on human resources, and labour costs are significant. Although many researchers have investigated construction labour productivity (CLP), the lack of adequate studies in this field is evident in developing countries. This paper intends to measure the CLP of the concrete pouring operations related to the construction of commercial-office complex projects in Iran. For this purpose, 19 critical factors with significant impact on the CLP were identified and listed in five groups, including individual, managerial, economic, technical, and environmental aspects. Then, a hybrid model based on artificial neural network (ANN) and Grasshopper optimisation algorithm (GOA) was developed to determine the most influential factors and increase the CLP model’s precision. Data related to the CLP of 24 under-construction commercial-office complex projects in Iran were gathered. Results reveal the most influencing factors on the CLP are labour experience and skill and motivation of labour from the individual group, the amount of pay from the economic group, site accidents from the technical group, proper supervision from the management group, and weather conditions from the environmental group. The findings can facilitate the development of more efficient project schedules, increasing the CLP, and reducing project costs.
KW - artificial neural network
KW - Construction labour productivity
KW - grasshopper optimisation algorithm
UR - http://www.scopus.com/inward/record.url?scp=85106237773&partnerID=8YFLogxK
U2 - 10.1080/15623599.2021.1927363
DO - 10.1080/15623599.2021.1927363
M3 - Article
AN - SCOPUS:85106237773
SN - 1562-3599
VL - 23
SP - 763
EP - 779
JO - International Journal of Construction Management
JF - International Journal of Construction Management
IS - 5
ER -