Life-cycle cost simulation and optimization modeling for coastal structures using Markov Chains and Genetic Algorithms

Ayman H. El Hakea, S. Abu-Samra
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Abstract

While extensive research has been carried out on the management of various types of infrastructure assets, limited research was allocated to coastal structures. The rapid world demographic growth especially in low-lying areas within close range to the shoreline over the past centuryas well as global climate change have given more importance to coastal infrastructure management. Climate change has increased storm intensities while decreasing storm return periods; imposing further risks to life and property. The aim of this research is to provide a modeling methodology for deterioration prediction, and optimization of repair, maintenance, and rehabilitation costs of various sorts of coastal protection structures. The coastal protection structures in Alexandria, Egypt, represent the case study. An Asset Inventory Database for Alexandria's coastal assets was developed, comprising 43 structures occupying an approximate length of 18.50 km. Established visual inspection and condition rating procedures were followed to obtain a current Structural Condition Index and a Structural Condition Matrix for each reach and each structure, considering a single inspection point in 2013. Structural Indices and Structural Condition Matrices (SCM's) are classified into severity ranges. Transition probabilities between structural condition ranges were calculated using backward analysis considering an excellent structural condition at the year of construction. Such probabilities were then utilized to formulate the structure's Markov Chain transition probability matrix, enabling the prediction of future deterioration. Integration of single-time random events, namely intermediate and design storms; was also performed on the future deterioration forecast. Maintenance and repair policies and their associated costs were determined, according to which a Genetic-Algorithm-based Life-Cycle Cost (LCC) optimization modeling was constructed with the aim to optimize maintenance and repair cost for the next 50 years, all while achieving the minimum reliability of structures. Results of the optimization are then presented collectively for the entire group of coastal structures within the study area.
基于马尔可夫链和遗传算法的海岸结构生命周期成本仿真与优化建模
虽然对各类基础设施资产的管理进行了广泛的研究,但对沿海结构的研究有限。在过去的一个世纪里,世界人口的快速增长,特别是在靠近海岸线的低洼地区,以及全球气候变化,使得沿海基础设施的管理更加重要。气候变化增加了风暴强度,减少了风暴复发期;对生命和财产造成进一步的威胁。本研究的目的是提供一种模型方法来预测和优化各种海岸防护结构的维修、维护和修复成本。埃及亚历山大港的海岸防护结构就是一个典型的案例。为亚历山大的沿海资产开发了一个资产清单数据库,其中包括43个结构,占地约18.50公里。考虑到2013年的单个检查点,遵循既定的目视检查和状态评级程序,获得每个河段和每个结构的当前结构状态指数和结构状态矩阵。结构指数和结构状态矩阵(SCM)被划分为不同的严重程度。考虑施工当年的优良结构条件,采用后向分析方法计算结构状态区间间的过渡概率。然后利用这些概率来制定结构的马尔可夫链转移概率矩阵,从而能够预测未来的恶化。整合单时间随机事件,即中间风暴和设计风暴;还对未来的恶化进行了预测。在确定维修策略及其相关成本的基础上,建立了基于遗传算法的全寿命周期成本(LCC)优化模型,以优化未来50年的维修成本,同时实现结构的最低可靠性。优化的结果,然后集体提出了整个组的沿海结构在研究区域。
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