Koichiro Nakatsu

Life-cycle Cost of Civil Infrastructure With Emphasis on Balancing Structural Performance and Seismic Risk of Road Network

The life-cycle cost (LCC) concepts and methods have impacted remarkably on the field of civil infrastructure. In this paper, LCC design concepts and methods are discussed with emphasis on bridges and road networks. The relationships among several performance measures are discussed. An attempt is made to provide rational balances of these measures by using a multi-objective genetic algorithm. Furthermore, LCC is evaluated focusing on the effects of earthquakes. At first, LCC analysis is formulated to consider the socio-economic effects due to the collapse of structures as well as the minimisation of lifetime maintenance cost. A stochastic model of structural response under earthquake effects is proposed. Then, the probability of failure of an individual bridge due to the earthquake excitation is calculated based on the reliability theory. In addition, LCC evaluations are performed not only for a single bridge but also for groups of bridges forming road networks.

Optimal Bridge Maintenance of Large Number of Bridges Using Robust Genetic Algorithm

In the maintenance planning, life cycle cost (LCC) can be reduced by treating multiple bridges simultaneously. However, it is difficult for a practical case to establish an optimal plan that reduces LCC because there is a large number of bridges to be maintained. In this study, an attempt is made to propose an optimal planning method for a large number of bridges in two stages by using a genetic algorithm (GA). In the first stage, the proposed method optimizes a maintenance plan for a single bridge with considering for preventive maintenance. Here, this optimal plan involves some periods in which maintenance works can be postponed without increasing LCC under the same safety condition. Then, in the second stage, the proposed method minimizes the total LCC of a large number of bridges by using each maintenance plan obtained for a single bridge, modifying it using such flexible periods involved in the maintenance plan, and calculating the total LCC by summing up the LCC for single bridge. Furthermore, the effects of budget constraints on LCC minimization are discussed. Numerical examples are presented to demonstrate the applicability of the proposed method.

Optimal Planning of Public Works Using Improved Genetic Algorithm

The purpose of this research is to establish an appropriate program for the long/middle term planning of public works. This planning needs to satisfy various constraints such as the order relations among construction projects and annual budget. Therefore, in order to solve this planning problem as a combinatorial optimization, it is necessary to search for solutions in very complicated solution space. In this optimization, Genetic Algorithm (GA) is more likely to be trapped by a local optimum due to the lack of diversity among individuals. In this study, an attempt is made to develop a decision support system for the optimal planning by using Improved GA. The proposed method adopts a local search which aims at satisfying constraint of the order relations. This local search can prevent that the search of GA tends to converge to a local optimum. Numerical examples are presented to demonstrate the applicability of the proposed method through the comparison with Simple GA computation.

Application of Evolutionary Computing to Disaster Restoration and Prevention Problems

In this paper, an attempt is made to apply evolutionary computing for earthquake disaster restoration and prevention problems. The relationships among early restoration, minimization of life-cycle cost and target safety level of road network are discussed by using multi-objective genetic algorithm. A comprehensive disaster prevention program is developed based on the recognition that road networks may be unavoidably damaged when very strong earthquakes occur. By introducing the concept of multi-objective optimization into the restoration and prevention scheduling for earthquake disasters, it is possible to find out several near-optimal restoration and prevention scheduling plans.

Application of Evolutionary Optimization in Structural Engineering

Practical optimization methods including genetic algorithms are introduced, based on evolutionary computing or soft computing. Several application examples are presented to demonstrate and discuss the efficiency and applicability of the described methods.