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Abdelkader, E M, Moselhi, O, Marzouk, M and Zayed, T (2021) Integrative Evolutionary-Based Method for Modeling and Optimizing Budget Assignment of Bridge Maintenance Priorities. Journal of Construction Engineering and Management, 147(09).

  • Type: Journal Article
  • Keywords: Bridge deck replacement; Discrete event simulation; Surrogate machine learning; Elman neural network; Invasive-weed optimization; Multiobjective differential evolution optimization;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0002113
  • Abstract:
    Recently, the number of deteriorating bridges has drastically increased. As such, enormous amounts of resources are invested yearly to maintain the performance of bridges within acceptable levels. This entails the development of bridge management systems to manage the imbalance between the extensive needs for maintenance, repair and rehabilitation actions, and the limited available funds. In this regard, the present study introduces a three-tier platform to model and allocate limited resources in maintenance projects. The first model involves building a discrete event simulation model to mimic the bridge deck replacement process. The second encompasses structuring an efficient and straightforward surrogate machine learning model for mimicking the computationally expensive discrete event simulation model. In the second phase, a novel hybrid Elman neural-network invasive-weed optimization model is developed for predicting the time, cost, greenhouse gases, and utilization rates of resource allocation plans using a database generated from the previous model. The third constitutes the formulation of a multiobjective differential evolution optimization model subject to the utilization rates of the involved resources and their dispersion. Results manifest superiority in cost prediction accuracies, achieving a mean absolute percentage error, mean absolute error, and root-mean-squared error of 4.873%, 78.466%, and 39.515%, respectively. Additionally, the developed multiobjective optimization model significantly outperformed a set of well-performing metaheuristics, yielding a hypervolume indicator, generational distance, spacing, diversity, spread, and coverage of 81.721%, 0.029%, 0.1881%, 0.5229%, 0.9618%, and 0.4087%, respectively. The results also demonstrate the developed multiobjective optimization model accomplished an improvement in the minimization time, cost, and greenhouse gases by 71.01%, 27.87%, and 39.29%, respectively, when compared against a genetic algorithm. The developed models are automated through the hybrid programming of C#.net and MATLAB. It is expected that the developed method can enable the practitioners and transportation agencies to establish timely-efficient, cost-effective, and sustainable resource allocation plans while accommodating the efficacious utilization of resources.

Ahmed, A, Mohammed, H A, Gambatese, J and Hurwitz, D (2021) Effects of Flashing Blue Lights Mounted on Paving Equipment on Vehicle Speed Behavior in Work Zones. Journal of Construction Engineering and Management, 147(09).

Cai, Q, Hu, Q and Ma, G (2021) Improved Hybrid Reasoning Approach to Safety Risk Perception under Uncertainty for Mountain Tunnel Construction. Journal of Construction Engineering and Management, 147(09).

Chen, B, Yu, X, Dong, F, Zheng, C, Ding, G and Wu, W (2021) Compaction Quality Evaluation of Asphalt Pavement Based on Intelligent Compaction Technology. Journal of Construction Engineering and Management, 147(09).

Hajj, C E, Jawad, D and Montes, G M (2021) Analysis of a Construction Innovative Solution from the Perspective of an Information System Theory. Journal of Construction Engineering and Management, 147(09).

Hasan, A, Rameezdeen, R, Baroudi, B and Ahn, S (2021) Mobile ICT–Induced Informal Work in the Construction Industry: Boundary Management Approaches and Consequences. Journal of Construction Engineering and Management, 147(09).

Hassan, F u, Le, T and Lv, X (2021) Addressing Legal and Contractual Matters in Construction Using Natural Language Processing: A Critical Review. Journal of Construction Engineering and Management, 147(09).

Huang, H, Zhang, C and Hammad, A (2021) Effective Scanning Range Estimation for Using TLS in Construction Projects. Journal of Construction Engineering and Management, 147(09).

Jiang, Y and Bai, Y (2021) Low–High Orthoimage Pairs-Based 3D Reconstruction for Elevation Determination Using Drone. Journal of Construction Engineering and Management, 147(09).

Johannes, K, Theodorus Voordijk, J, Marias Adriaanse, A and Aranda-Mena, G (2021) Identifying Maturity Dimensions for Smart Maintenance Management of Constructed Assets: A Multiple Case Study. Journal of Construction Engineering and Management, 147(09).

Kar, S and Jha, K N (2021) Exploring the Critical Barriers to and Enablers of Sustainable Material Management Practices in the Construction Industry. Journal of Construction Engineering and Management, 147(09).

Noghabaei, M, Han, K and Albert, A (2021) Feasibility Study to Identify Brain Activity and Eye-Tracking Features for Assessing Hazard Recognition Using Consumer-Grade Wearables in an Immersive Virtual Environment. Journal of Construction Engineering and Management, 147(09).

Perez-Perez, Y, Golparvar-Fard, M and El-Rayes, K (2021) Scan2BIM-NET: Deep Learning Method for Segmentation of Point Clouds for Scan-to-BIM. Journal of Construction Engineering and Management, 147(09).

Raoufi, M and Fayek, A R (2021) How to Improve Crew Motivation and Performance on Construction Sites. Journal of Construction Engineering and Management, 147(09).

Taghizadeh, K, Alizadeh, M and Yavari Roushan, T (2021) Cooperative Game Theory Solution to Design Liability Assignment Issues in BIM Projects. Journal of Construction Engineering and Management, 147(09).

Votto, R, Lee Ho, L and Berssaneti, F (2021) Earned Duration Management Control Charts: Role of Control Limit Width Definition for Construction Project Duration Monitoring. Journal of Construction Engineering and Management, 147(09).

Xu, Y, Shen, X, Lim, S and Li, X (2021) Three-Dimensional Object Detection with Deep Neural Networks for Automatic As-Built Reconstruction. Journal of Construction Engineering and Management, 147(09).