Integrated Simulation Model for Maintenance and Repair Optimisation for Rubble Mound Coastal Structures Using Markov Chains, Regression and Genetic Algorithms

The significant increase in the world population living within close proximity to coastlines has assigned further importance to coastal protection structures. This importance has been even ascertained given the increasing risks posed by climate change. from this standpoint comes the importance of maintenance and repair strategies for coastal protection structures especially in low-lying coastal areas. This research provides an integrated model for the optimisation of maintenance and repair for rubble-mound breakwaters, revetments and groins under simulated climatic conditions. The model starts by establishing an Asset Inventory Database (AID), a Markov-Chain (MC) Deterioration Engine, and a Genetic Algorithm (GA) repair and maintenance Optimisation Engine. The AID includes the coastal structures within any particular study area, along with their design attributes and hydrodynamic data. The database divides coastal structures into structural reaches for ease of management. The MC deterioration engine predicts future condition of the structure based upon actual visual inspection results, while taking into account the single-time condition drop caused by seasonal storms. The GA Optimisation Engine includes a set of decisions that are triggered when the structure's Priority Index (PI) -- a factor of the condition and the magnitude of failure impact- attains the defined threshold. MC deterioration patterns are expressed using best-fit regression to enable the integration between MC's and the GA Optimisation Engine. The case study consists of a group of rubble-mound structures in Alexandria, Egypt. The Optimisation Engine simulates repair and maintenance scenarios for various climatic conditions at a preset PI threshold, and results are compared and discussed.