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Aguado, A, Caño, A d, de la Cruz, M P, Gómez, D and Josa, A (2012) Sustainability Assessment of Concrete Structures within the Spanish Structural Concrete Code. Journal of Construction Engineering and Management, 138(02), 268–76.
Art Chaovalitwongse, W, Wang, W, Williams, T P and Chaovalitwongse, P (2012) Data Mining Framework to Optimize the Bid Selection Policy for Competitively Bid Highway Construction Projects. Journal of Construction Engineering and Management, 138(02), 277–86.
Beainy, F, Commuri, S and Zaman, M (2012) Quality Assurance of Hot Mix Asphalt Pavements Using the Intelligent Asphalt Compaction Analyzer. Journal of Construction Engineering and Management, 138(02), 178–87.
Cattaneo, S and Mola, F (2012) Assessing the Quality Control of Self-Consolidating Concrete Properties. Journal of Construction Engineering and Management, 138(02), 197–205.
Cha, H S, Kim, K H and Kim, C K (2012) Case Study on Selective Demolition Method for Refurbishing Deteriorated Residential Apartments. Journal of Construction Engineering and Management, 138(02), 294–303.
Cho, Y K, Bode, T, Song, J and Jeong, J (2012) Thermography-Driven Distress Prediction from Hot Mix Asphalt Road Paving Construction. Journal of Construction Engineering and Management, 138(02), 206–14.
Fan, S (2012) Modified Time Impact Analysis Method. Journal of Construction Engineering and Management, 138(02), 227–33.
Gambatese, J A and Rajendran, S (2012) Flagger Illumination during Nighttime Construction and Maintenance Operations. Journal of Construction Engineering and Management, 138(02), 250–7.
Jarkas, A M (2012) Influence of Buildability Factors on Rebar Installation Labor Productivity of Columns. Journal of Construction Engineering and Management, 138(02), 258–67.
Lingard, H, Cooke, T and Blismas, N (2012) Do Perceptions of Supervisors’ Safety Responses Mediate the Relationship between Perceptions of the Organizational Safety Climate and Incident Rates in the Construction Supply Chain?. Journal of Construction Engineering and Management, 138(02), 234–41.
Montemanni, R, Toklu, N E, Toklu, & & and Toklu, Y C (2012) Aggregate Blending via Robust Linear Programming. Journal of Construction Engineering and Management, 138(02), 188–96.
Nieto-Morote, A and Ruz-Vila, F (2012) Last Planner Control System Applied to a Chemical Plant Construction. Journal of Construction Engineering and Management, 138(02), 287–93.
Tserng, H P, Russell, J S, Hsu, C and Lin, C (2012) Analyzing the Role of National PPP Units in Promoting PPPs: Using New Institutional Economics and a Case Study. Journal of Construction Engineering and Management, 138(02), 242–9.
Wu, M, Wing Chau, K, Shen, Q and Yin Shen, L (2012) Net Asset Value–Based Concession Duration Model for BOT Contracts. Journal of Construction Engineering and Management, 138(02), 304–8.
Yang, I, Hsieh, Y and Kung, L (2012) Parallel Computing Platform for Multiobjective Simulation Optimization of Bridge Maintenance Planning. Journal of Construction Engineering and Management, 138(02), 215–26.
- Type: Journal Article
- Keywords: Maintenance; Bridges; Construction costs; Optimization; Simulation; Computation; Maintenance planning; Bridge; Cost; Multiobjective optimization; Simulation optimization; Particle swarm optimization; Parallel computing;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000421
- Abstract:
The maintenance planning of deteriorating bridges is to find a balance between obtained performance and incurred cost. Because the planning horizon spans tens of years, a certain amount of uncertainty is inherent in forecasting the deteriorating process and the costs and effects of maintenance actions. This paper proposes a multiobjective simulation optimization framework to establish the trade-off among the expected values of life-cycle maintenance cost and of the performance measures. The trade-off information, represented as the Pareto front, gives planners sufficient flexibility to respond to various needs. The optimization is performed by a multiobjective particle swarm optimization (MOPSO) algorithm, while Monte Carlo simulation is used to model the uncertainties. To alleviate the computational burden, the proposed framework is implemented in a parallel computing platform, where three programming paradigms (master-slave, island, and diffusion) are developed to distribute computation across processors and to control interprocessor communication. The validity of the proposed framework, along with the parallel paradigms, is investigated through a practical case. It is shown statistically that the proposed MOPSO algorithm is superior to the well-known nondominating sorting genetic algorithm II as the former can obtain a better Pareto front, whose convergence and diversity are measured together by the hypervolume indicator. Both the island and diffusion paradigms, being loosely synchronous, exhibit high efficiency and good scalability as they achieve superlinear speedups. The island paradigm outperforms the other two in terms of improved solution quality within fixed time.