TY - GEN
T1 - Case Machine Learning Experience into Modelling and Simulation to Improve the Efficiency of Construction Engineering Process Management
AU - Alanya-Beltran, Joel
AU - Kanwer, Budesh
AU - Buddhi, Dharam
AU - Jeelani, Syed Hamim
AU - Valderrama-Zapata, Carlos
AU - Jaiswal, Sushma
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - The research work has highlighted that the main purpose of this research work is to identify the role of ML (Machine Learning) in simulation modelling and its effect on construction project engineering. In this research, it has been clearly identified that ML achieves the advantages such as risk identification, quality enhancement, and reduction of financial burden. These critical aspects are integrated into the engineering of the construction projects enabled by ML. The present research also shed light on the machine learning technologies for the completion of a construction project with much ease. Lastly, the research consists of some of the data collection methods by which the researcher could appropriately gather the research data and information for the smooth completion of the construction project.
AB - The research work has highlighted that the main purpose of this research work is to identify the role of ML (Machine Learning) in simulation modelling and its effect on construction project engineering. In this research, it has been clearly identified that ML achieves the advantages such as risk identification, quality enhancement, and reduction of financial burden. These critical aspects are integrated into the engineering of the construction projects enabled by ML. The present research also shed light on the machine learning technologies for the completion of a construction project with much ease. Lastly, the research consists of some of the data collection methods by which the researcher could appropriately gather the research data and information for the smooth completion of the construction project.
KW - algorithms
KW - construction engineering
KW - Machine learning
KW - simulation
UR - http://www.scopus.com/inward/record.url?scp=85135472092&partnerID=8YFLogxK
U2 - 10.1109/icacite53722.2022.9823866
DO - 10.1109/icacite53722.2022.9823866
M3 - Contribución a la conferencia
AN - SCOPUS:85135472092
SN - 9781665437899
T3 - 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022
SP - 2463
EP - 2467
BT - 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 April 2022 through 29 April 2022
ER -