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Akin, F D, Damci, A and Arditi, D (2024) Optimum finance-based scheduling. Journal of Construction Engineering and Management, 150(09).
Assaad, R H, Omran, A, Soliman, N and Assaf, G (2024) Prediction of the lateral pressure of self-consolidating concrete on construction formwork systems using machine-learning algorithms. Journal of Construction Engineering and Management, 150(09).
Assaf, G and Assaad, R H (2024) Analyzing the critical success factors affecting project bundling performance of infrastructure projects. Journal of Construction Engineering and Management, 150(09).
Chammout, B, El-Adaway, I H, Abdul Nabi, M and Assaad, R H (2024) Price escalation in construction projects: Examining national and international contracts. Journal of Construction Engineering and Management, 150(09).
Dorignon, L, Oswald, D, Kempton, L, Boehme, T, Iyer-Raniga, U, Moore, T and Dalton, T (2024) Investigating residential building materials in a circular economy: An Australian perspective. Journal of Construction Engineering and Management, 150(09).
Görsch, C, Seppänen, O, Peltokorpi, A and Lavikka, R (2024) Unlocking productivity: Revealing waste and hidden disturbances impacting mep workers. Journal of Construction Engineering and Management, 150(09).
Gao, M Y, Han, J, Yang, Y, Tiong, R L K, Zhao, C and Han, C (2024) BIM-based and IoT-driven smart tracking for precast construction dynamic scheduling. Journal of Construction Engineering and Management, 150(09).
Jiang, Y, Yang, G, Li, H, Zhang, T and Khudhair, A (2024) Physics-informed knowledge-driven decision-making framework for holistic bridge maintenance. Journal of Construction Engineering and Management, 150(09).
- Type: Journal Article
- Keywords: bridge maintenance; finite element method; holistic decision-making; knowledge engineering; ontology; semantic reasoning; semantic web; web ontology language
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13593
- Abstract:
Bridge maintenance is a highly intricate task that involves considering a wide range of factors in order to achieve optimal decisions that align with multiple objectives, criteria, and the entire lifecycle of the bridge. While physics-informed analysis, such as the finite element method (FEM), can simulate complex and closely coupled scenarios, such as bridge structural analysis, it cannot account for some loosely coupled discrete factors, which could be addressed by ontological reasoning. Therefore, this paper presents a knowledge-driven decision-making framework that combines static knowledge reasoning with dynamic FEM analysis results to support holistic bridge maintenance decisions. One significant contribution of this research is the development of a comprehensive bridge maintenance ontology that incorporates knowledge derived from bridge maintenance standards. Another key contribution is the ability to employ complex runtime rules-based reasoning to tackle intricate bridge maintenance scenarios. To enable automatic knowledge-driven reasoning, an integrated workflow is developed to orchestrate semantic modeling with numerical modeling through a Python-based Web Ontology Language application programming interface (OWL API). This integration facilitates the efficient orchestration of the framework. A case study is presented to demonstrate the potential for the developed framework in assisting with the complex holistic decisions required for bridge maintenance.
Koo, H J, Kelly, D and Deman, D (2024) Risk assessment of the challenges in the adaptive reuse of historic buildings. Journal of Construction Engineering and Management, 150(09).
Namian, M, Nabil, F R, Al-Mhdawi, M K S, Kermanshachi, S S and Nnaji, C (2024) Postpandemic era: Investigating the impact of COVID-19 on construction workers' situational awareness. Journal of Construction Engineering and Management, 150(09).
Ning, X, Yang, Y, Liu, C and Han, Y (2024) Construction workers' unsafe behavior contagion under government-contractor dual influence. Journal of Construction Engineering and Management, 150(09).
Olugboyega, O (2024) Diverse forms of greed and self-interest that contribute to corruption among construction stakeholders. Journal of Construction Engineering and Management, 150(09).
Ouyang, Y and Luo, X (2024) Effects of physical fatigue on construction workers' visual search patterns during hazard identification. Journal of Construction Engineering and Management, 150(09).
Paneru, S, Suh, S, Seo, W and Rausch, C (2024) Evaluating the decarbonization potential of industrialized construction: A review of the current state, opportunities, and challenges. Journal of Construction Engineering and Management, 150(09).
Sadoughi, A, Kouhirostami, M, Kouhirostamkolaei, M, Qi, B and Costin, A (2024) Autonomous building design for manufacturing and assembly: A systematic review of design application, challenges, and opportunities. Journal of Construction Engineering and Management, 150(09).
Shahedi, F, Etemadfard, H, Omrani, F and Ghalehnovi, M (2024) Cost performance modeling for steel fabrication shops with machine learning algorithms. Journal of Construction Engineering and Management, 150(09).
Shuang, Q, Liu, X, Wang, Z and Xu, X (2024) Automatically categorizing construction accident narratives using the deep-learning model with a class-imbalance treatment technique. Journal of Construction Engineering and Management, 150(09).
Wang, R D, Zayed, T, Eltoukhy, A E E and Wu, H (2024) Integrated planning approach for optimizing tower crane and truck locations in modular integrated construction. Journal of Construction Engineering and Management, 150(09).
Wu, H, Chang, Y and Chen, Y (2024) Transitioning work arrangements in the construction industry: Changes in time-use patterns and individual greenhouse gas emissions. Journal of Construction Engineering and Management, 150(09).
Yang, J, Wu, Y and Li, D (2024) Block planning based on grid's topology for the dismantling of long-span spatial lattice structures. Journal of Construction Engineering and Management, 150(09).
Zhang, Y, Chang, R, Mao, W, Zuo, J, Liu, L and Han, Y (2024) Challenges of automating interior construction progress monitoring. Journal of Construction Engineering and Management, 150(09).
Zhang, Y, Liu, L, Song, Z, Zhao, Y and He, S (2024) Enhancing tunnel boring machine penetration rate predictions through particle swarm optimization and elman neural networks. Journal of Construction Engineering and Management, 150(09).