Integrated life-cycle cost & risk optimization framework for coastal protection structures - El Hakea, Ayman Hassan Abdel-Wahab (AUC Knowledge Fountain, 2015)
While extensive research has been carried out on the management of various types of infrastructure assets, limited research has been carried out for coastal structures. The rapid growth of the world population living in low-lying areas within close range to the shoreline over the past century compounded by the impact of global climate change on shoreline hydrodynamics; have increased the importance of coastal infrastructure management. Climate change has recently increased storm intensities in addition to decreasing storm return periods; imposing greater risks to life and property. The aim of this research is to provide an artificial-intelligence-based framework for coastal protection structures, which is capable of predicting structural deterioration patterns, and accordingly offers the end user the capability of optimization of repair, maintenance, and rehabilitation costs, in addition to the optimization of risk exposure limits under pre-defined budgetary constraints. For this purpose, an Asset Inventory Database (AID) for coastal assets is developed, comprising the design, environmental, and historical data pertaining to coastal assets. Established visual inspection and condition rating procedures are followed to obtain the values for the Structural Condition Index (SI) and a Structural Condition Matrix (SCM) for individual structures, considering a single inspection point. This takes into account cases where no previous inspection and condition rating records are available. SI’s are in their turns classified into severity ranges. Functional Condition Indices (FI's) are also calculated for submerged structures that could not be visually inspected and taken as the equivalent to the Condition Index (CI). Deterioration Transition Matrices (DTM's), including transition probabilities between each of the deterioration severity ranges are next calculated using backward Markov-Chain (MC) analysis. Such probabilities are then utilized to formulate the Markovian Deterioration Transition Matrix (DTM) for each individual sub-reach and hence each individual structure; enabling the prediction of future deterioration. The trends obtained from this forward Markovian deterioration modeling are approximated by mathematical functions using best-fit regression. The single-time deterioration effect of design and intermediate storms is also considered by virtue of the Storm Simulator feature. By calculating the average maintenance and repair per meter run of every coastal structure, corresponding to the condition of the structure, a Genetic-Algorithm (GA) – based Life-Cycle Cost (LCC) optimization modeling is then developed with the aim to minimize the total LCC for the entire coastal assets up to year 2050, while achieving the minimum reliability of structures, expressed as a Priority Index (PI). PI's are numerical values that are factors in the condition state of the structure and its criticality with respect to risk to life and property upon failure. In parallel, another optimization module aims at minimizing the total risk exposure level under various budget scenarios. Both the LCC and risk optimization modules were run for various scenarios of storm occurrences to account for the effect of global climate change. The considered case study in this research is a group of 43 different structures in Alexandria, Egypt. It was found that under stringent climatic conditions, the required LCC to maintain coastal structures at the desired level of reliability increases dramatically as opposed to normal climatic conditions. In addition, it was observed that the risk to life and property decreases with the increase of available budget for maintenance and repair. Further, the suggested framework was observed to be more cost-efficient than the common maintenance and repair strategies, in terms of keeping the maximum acceptable PI threshold.