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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).
- Type: Journal Article
- Keywords: activity sequencing; daily work report; highway projects; long short-term memory; machine learning; project scheduling
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13165
- Abstract:
It is critical for project owners to have a reasonable estimation of project duration before the letting date for contract time determination and project management purposes. To determine the project duration, highway agencies employ scheduling techniques and arrange activities in sequential order. Activity sequencing is a crucial task since a slight change in the sequence of critical activities can significantly influence project duration. Also, the task of activity arrangement is time-consuming for a broad portfolio of projects and requires skillful schedulers. To aid activity sequence determination, prior studies used project drawings, expert knowledge, and historical data to identify sequence rules, logic templates, and sequence prediction models. However, weaknesses and areas of improvement exist, including a lack of adequately leveraging available historical data, the necessity of human input, reliance on human experience rather than data, and poor detection of the overlapping time of activities. This study proposes a novel framework that predicts the sequences of work activities using historical daily work reports to train a long short-term memory recurrent neural network to predict the activity sequence and overlapping in future projects. The daily work reports of 720 highway projects obtained from a highway agency are used as the case study. A novel evaluation technique based on conditional probability is used to assess the model and compare its output sequence to sequences created randomly. The assessment results indicate that the model's output is superior in 94.4% of situations, suggesting a high level of model reliability. The impact of key project characteristics such as project work type and size on activity prediction is examined, indicating a significant impact of project work type and no impact of project size on activity prediction. The results of this study can assist highway project owners in activity sequence and overlap determination by entering a series of activities and receiving the likely next successors.
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).
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).