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Al-Mhdawi, M K S, Brito, M, Onggo, B S, Qazi, A, O'Connor, A and Namian, M (2023) Construction risk management in Iraq during the COVID-19 pandemic: Challenges to implementation and efficacy of practices. Journal of Construction Engineering and Management, 149(09).
Alankarage, S, Chileshe, N, Samaraweera, A, Rameezdeen, R and Edwards, D J (2023) Guidelines for using a case study approach in construction culture research: Application to BIM-enabled organizations. Journal of Construction Engineering and Management, 149(09).
Alikhani, H, Le, C, Jeong, H D and Damnjanovic, I (2023) Sequential machine learning for activity sequence prediction from daily work report data. Journal of Construction Engineering and Management, 149(09).
Cuciniello, G, Inzerillo, G, Corazziari, L, Ciampini, A, Degni, R, Torresi, M and Leandri, P (2023) Modeling the tire-pavement noise as a regression function of speed, mixture properties, and age of the layer. Journal of Construction Engineering and Management, 149(09).
Deng, J, Zhao, Y, Li, X, Wang, Y and Zhou, Y (2023) Network embeddedness, relationship norms, and cooperative behavior: Analysis based on evolution of construction project network. Journal of Construction Engineering and Management, 149(09).
Dhanshyam, M and Srivastava, S K (2023) Decision framework for efficient risk mitigation in BOT highway infrastructure service projects. Journal of Construction Engineering and Management, 149(09).
Hochscheid, E, Falardeau, M, Lapalme, J, Boton, C and Rivest, L (2023) Practitioners' concerns about their liability toward BIM collaborative digital mockups: Case study in civil engineering. Journal of Construction Engineering and Management, 149(09).
Jiang, F, Lyu, Y, Zhang, Y and Guo, Y (2023) Research on the differences between risk-factor attention and risk losses in PPP projects. Journal of Construction Engineering and Management, 149(09).
Kong, Z, Ma, H, Lv, K and Shi, J J (2023) Liability of foreignness in public-private partnership projects. Journal of Construction Engineering and Management, 149(09).
Lei, Z, Sadiq Altaf, M, Cheng, Z, Liu, H and Tang, S (2023) Measurement of information loss and transfer impacts of technology systems in offsite construction processes. Journal of Construction Engineering and Management, 149(09).
Li, J, Yang, M, Liu, C, Li, A and Guo, B (2023) Listen to the companies: Exploring BIM job competency requirements by text mining of recruitment information in China. Journal of Construction Engineering and Management, 149(09).
Liang, R, Li, R and Chong, H Y (2023) Decision-making model for evaluating joint venture contractors in construction of complex infrastructure megaprojects. Journal of Construction Engineering and Management, 149(09).
Ma, H, Cao, S, Wang, Y and Zhang, H (2023) The moderating effect of optimism bias on ambivalence of workers' unsafe behaviors. Journal of Construction Engineering and Management, 149(09).
Ma, J, Li, H, Yu, X, Fang, X, Fang, B, Zhao, Z, Huang, X, Anwer, S and Xing, X (2023) Sweat analysis-based fatigue monitoring during construction rebar bending tasks. Journal of Construction Engineering and Management, 149(09).
Montalbán-Domingo, L, Torres-Machi, C, Sanz-Benlloch, A, Pellicer, E and Molenaar, K R (2023) Green public procurement in civil infrastructure construction: Current performance and main project characteristics. Journal of Construction Engineering and Management, 149(09).
Mostofi, F, Tokdemir, O B and Toǧan, V (2023) Comprehensive root cause analysis of construction defects using semisupervised graph representation learning. Journal of Construction Engineering and Management, 149(09).
Patil, K R, Bhandari, S, Agrawal, A, Ayer, S K, Perry, L A and Hallowell, M R (2023) Analysis of youtube comments to inform the design of virtual reality training simulations to target emotional arousal. Journal of Construction Engineering and Management, 149(09).
Prieto, A J and Alarcón, L F (2023) Using fuzzy inference systems for lean management strategies in construction project delivery. Journal of Construction Engineering and Management, 149(09).
- Type: Journal Article
- Keywords: digital tools; fuzzy logic; lean construction; multiple linear regression; wastes management
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-12922
- Abstract:
When using lean waste management in construction project delivery, computational methodologies are currently an innovative technology for the implementation of efficient and effective improvement strategies in the development of Industry 4.0 in Chile. Lean models are able to manage data obtained from construction projects along with the data obtained from the knowledge base of professional experts (expert survey). The waste management of construction projects under the lean philosophy requires cooperative efforts, where the opinion of professional experts is completely paramount to analyze multidisciplinary knowledge. Therefore, new protocols and disruptive procedures based on artificial intelligence (AI) tools can help decision makers prioritize activities, minimize uncertainty, and avoid wasteful actions that add no value to the project and thus can be minimized or completely eliminated. The vagueness of subjective human judgment in the degree of application of lean waste management in project delivery is modeled by a fuzzy logic model that includes additional considerations related to the lean implementation. Moreover, multiple linear regression analysis has been implemented in order to verify and validate the previous digital fuzzy model. In this sense, the main aim of this study is to develop new approaches regarding AI systems, using fuzzy sets and multiple linear regression for managing waste in construction project delivery in the metropolitan area of Santiago, Chile. A theorized application of the models reveals that the sample (100 construction projects) can be classified into three lean waste condition levels: high, medium, or low waste effects. The outcomes of this research will contribute to the Chilean construction industry environment and will open new ways for harnessing AI-based technology in the construction industry to the fullest potential, to achieve better time and cost predictability with a client- and end-user-centered world view.
Pu, H, Fan, X, Li, W, Zhang, W, Schonfeld, P, Wei, F and Xu, Z (2023) Realizing a quick partial BIM update of subgrade in railway stations. Journal of Construction Engineering and Management, 149(09).
Qin, Y and Bulbul, T (2023) An EEG-based mental workload evaluation for ar head-mounted display use in construction assembly tasks. Journal of Construction Engineering and Management, 149(09).
Ranasinghe, U, Jefferies, M, Davis, P and Pillay, M (2023) Enabling a resilient work environment: An analysis of causal relationships between resilience engineering factors in construction refurbishment projects. Journal of Construction Engineering and Management, 149(09).
Ren, X, Li, Y and Guo, M (2023) Dynamically identifying and evaluating key barriers to promoting prefabricated buildings: Text mining approach. Journal of Construction Engineering and Management, 149(09).
Xiang, Z, Rashidi, A and Ou, G (2023) Integrating inverse photogrammetry and a deep learning-based point cloud segmentation approach for automated generation of BIM models. Journal of Construction Engineering and Management, 149(09).
Zhou, X and Liao, P C (2023) Weighing votes in human-machine collaboration for hazard recognition: Inferring a hazard-based perceptual threshold and decision confidence from electroencephalogram wavelets. Journal of Construction Engineering and Management, 149(09).