TY - JOUR
T1 - Machine learning techniques applied to construction
T2 - A hybrid bibliometric analysis of advances and future directions
AU - Garcia, Jose
AU - Villavicencio, Gabriel
AU - Altimiras, Francisco
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Minatogawa, Vinicius
AU - Franco, Matheus
AU - Martínez-Muñoz, David
AU - Yepes, Víctor
N1 - Funding Information:
José García was supported by the Grant CONICYT/FONDECYT/INICIACION/, Chile 11180056.José García and Vinicius Minatogawa was supported by PROYECTO DI INVESTIGACIÓN INNOVADORA INTERDISCIPLINARIA, Chile: 039.414/2021.Víctor Yepes was supported by Grant PID2020-117056RB-I00 funded by MCIN/AEI/, Spain 10.13039/501100011033 and by “ERDF A way of making Europe”.Francisco Altimiras was supported by the INF-PUCV Scholarship, Chile.Broderick Crawford is supported by Grant CONICYT/FONDECYT/ REGULAR/1210810, Chile.
Publisher Copyright:
© 2022
PY - 2022/10
Y1 - 2022/10
N2 - Complex industrial problems coupled with the availability of a more robust computing infrastructure present many challenges and opportunities for machine learning (ML) in the construction industry. This paper reviews the ML techniques applied to the construction industry, mainly to identify areas of application and future projection in this industry. Studies from 2015 to 2022 were analyzed to assess the latest applications of ML techniques in construction. A methodology was proposed that automatically identifies topics through the analysis of abstracts using the Bidirectional Encoder Representations from Transformers technique to select main topics manually subsequently. Relevant categories of machine learning applications in construction were identified and analyzed, including applications in concrete technology, retaining wall design, pavement engineering, tunneling, and construction management. Multiple techniques were discussed, including various supervised, deep, and evolutionary ML algorithms. This review study provides future guidelines to researchers regarding ML applications in construction.
AB - Complex industrial problems coupled with the availability of a more robust computing infrastructure present many challenges and opportunities for machine learning (ML) in the construction industry. This paper reviews the ML techniques applied to the construction industry, mainly to identify areas of application and future projection in this industry. Studies from 2015 to 2022 were analyzed to assess the latest applications of ML techniques in construction. A methodology was proposed that automatically identifies topics through the analysis of abstracts using the Bidirectional Encoder Representations from Transformers technique to select main topics manually subsequently. Relevant categories of machine learning applications in construction were identified and analyzed, including applications in concrete technology, retaining wall design, pavement engineering, tunneling, and construction management. Multiple techniques were discussed, including various supervised, deep, and evolutionary ML algorithms. This review study provides future guidelines to researchers regarding ML applications in construction.
KW - BERT
KW - Concretes
KW - Construction
KW - Construction management
KW - Machine learning
KW - Pavements
KW - Retaining walls
KW - Tunnels
UR - http://www.scopus.com/inward/record.url?scp=85136311338&partnerID=8YFLogxK
U2 - 10.1016/j.autcon.2022.104532
DO - 10.1016/j.autcon.2022.104532
M3 - Review article
AN - SCOPUS:85136311338
VL - 142
JO - Automation in Construction
JF - Automation in Construction
SN - 0926-5805
M1 - 104532
ER -