遺伝的アルゴリズムを用いたコンクリート橋梁群の最適維持管理計画の策定

: The Navy, like many other federal organizations, is being faced with a decreasing budget. One of the Navy's biggest problems is that owning and operating their shore infrastructure is consuming too much of its limited resources. One way to reduce the cost of owning and operating the shore infrastructure is to plan and design facilities with lower life cycle costs i.e., facilities with components that last longer, cost less to operate, and cost less to maintain. Naval Facilities Engineering Command (NAVFAC), as with all federal agencies, has been directed to perform economic analysis based on Life Cycle Cost (LCC) concepts on all Navy facility projects. However, in a recent study, NAVFAC found that a majority of their facility project areas were not using LCC concepts when conducting economic analysis. This paper will: (1) provide a brief introduction to life cycle cost concepts and economic analysis; (2) introduce NAVFAC and its role in the facility planning process; (3) summarize current federal policies regarding facility planning and LCC concepts; (4) summarize NAVFAC's report concerning the status of LCC in the planning process; (5) analyze and make recommendations to two main issues hindering NAVFAC use of LCC.

In order to establish a rational bridge management program, it is necessary to develop a cost-effective decision-support system for the maintenance of bridges. In this paper, an attempt is made to develop a bridge management system that can provide practical maintenance plans by using an improved multi-objective genetic algorithm. A group of bridges is analyzed to demonstrate the applicability and efficiency of the proposed method. INTRODUCTIONIn Japan, maintenance work on bridge structures is increasing in volume and importance. The number of structures requiring repair or replacement will drastically increase in the coming ten years. In order to establish a rational maintenance program, the concept of Life-Cycle Cost (LCC) has gained great attention, which minimizes the total cost of whole lives of structures [1]. So far, the authors have developed LCC based bridge maintenance systems for existing concrete bridge structures [2, 3]. Concrete bridges are deteriorating due to the corrosion of reinforcing bars and the neutralization of concrete. Then, it is necessary to achieve an optimal maintenance plan that can provide appropriate methods and times of repair or replacement. However, the optimal maintenance problem is very difficult to solve, because it is one of combinatorial problems with discrete design variables and discontinuous objective functions. Furthermore, the tougher problem becomes , the larger and more complex it is. Although low-cost maintenance plans are desirable for bridge owners it is necessary to consider lifetime constraints when choosing an appropriate maintenance program. For example, the minimization of maintenance cost needs the target safety level and the expected service lifetime to be both prescribed. The predetermination of requirements may affect the possible maintenance plans. Namely, it may be possible to find out a better solution that can largely extend the service life if the safety level can be slightly decreased using the same amount of maintenance cost [4, 5]. In this paper, an attempt is made to improve the bridge management system proposed in [6], by developing an improved multi-objective genetic algorithm. Many alternative 17 ANALYSIS AND COMPUTATION SPECIALTY CONFERENCE th Copyright ASCE 2006 17th Analysis and Computation Conference maintenance plans with different characteristics can be obtained by introducing the concept of multi-objective optimization. Single-objective optimization can provide various solutions by changing the constraints. However, this approach requires enormous computation time. When selecting a practical maintenance plan, it is essential to compare feasible solutions obtained under various conditions. This process is effective for the accountability through the disclosure of information. A new multi-objective genetic algorithm is developed for the bridge management problems that have a lot of constraints. Several numerical examples are presented to demonstrate the applicability and efficiency of the proposed method.

Life Cycle Costing (LCC) is a tool, which can evaluate all the building-related costs over a specified period of time, as mentioned in the agreed scope. Though there are number of benefits, LCC practices of consultants in the building industry are claimed to be weak. Hence, the aim of this research is to investigate barriers in practicing LCC techniques experienced by Sri Lankan quantity surveyors. Accordingly, a mixed approached was followed in data collection through a questionnaires survey and a series of expert interviews. However, in Sri Lanka, building construction consultants are not practicing the LCC concept to a greater extent. This research identified, lack of awareness on the LCC tool by employers and practitioners, lack of knowledge on LCC and lack of previous data as the major barriers for practicing LCC techniques in local context. It was suggested that enhancing the practice of LCC through conducting various awareness programme on LCC concept, build up and maintain the proper database and introducing user friendly tools, applications, calculations methods, guidelines and regulations, will lead to avoid above mentioned barriers, which will ultimately enhance the proper practice of LCC concept in the Sri Lankan quantity surveyors.

Our project cost control is mostly concentrated in the construction phase cost control , tends to ignore the costs of the operational phase control.For the maintenance and repair of the existing bridge structure , this paper proposed the concept of life-cycle cost,had a detailed analysis of the composition of the life cycle cost.Based on the discount rate , and used the application of engineering principles of economics,proposed optimization mathematical model of the life cycle cost ,and gave the life-cycle cost analysis for bridge maintenance program with examples ,verified the validity of the model and the implementation of the need for life-cycle cost analysis, and it had practical significance for bridge maintenance phase of cost control.

Background/Objective: The objective is to minimize ambiguous test cases by using Adaptive Genetic Algorithm (AGA). Problem description/Proposed Method: Here we are concerned with the problem of randomly generated test cases. It can contain some ambiguous test cases, which lead to problems at the organizational level. A random algorithm will generate random test cases each time it is run, and it will have resemblance each time. Another problem related to random algorithms is that running them can take a lot of time. To minimize these issues we propose a new technique, which will reduce the given drawbacks. We proposed an Adaptive Genetic Algorithm (AGA), which will provide legal input in each case where it applied. Thus the problem of ambiguity will decrease. Results/Findings: In this research, the near optimal inputs will be generated based on the Adaptive Genetic Algorithm (AGA), which will reduce the illegal inputs. The fault detection rate is used as the fitness function in AGA. To remove the fault proneness, our AGA uses the coverage metrics of the test cases. Conclusion: Random algorithms will generate low cost test cases in large number but problem is that it will consists ambiguous test cases ,to reduce these here we are using AGA which will further reduce test cases by moderating the illegal inputs.

A holistic approach to risk-based decision on inspection and design of fatigue-sensitive structures

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation is directly controlled during evolution using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behavior of the algorithm.

In this paper we propose an adaptive genetic algorithm (AGA) to optimize wind farm layout in order to achieve a highest aggregate wind power conversion efficiency with the presence of wake effect impacts. Instead of using random “crossover” in each iteration as in conventional GA, our proposed adaptive algorithm introduces novel relocation of “bad” turbines so that turbines experiencing worst wake effect impacts in a layout will be relocated to some new and more efficient positions. Therefore each iteration of the AGA can more effectively improve the aggregate wind power conversion efficiency and greatly accelerate the algorithm convergence. We experiment the proposed AGA and compare its performance with conventional GA based on a number of scenarios such as multi-directional wind distribution, offshore and inland wind distribution, with sparse and crowded farm settings. Numerical results verify the effectiveness of the proposed AGA algorithm which is able to locate an optimal layout at a much faster convergence speed and achieve a higher aggregate wind power output from a farm.

Genetic algorithm (GA) is widely applied to optimal path planning of mobile robots. In this work, an adaptive genetic algorithm (AGA) is proposed, which is expected to solve some difficulties that conventional GA inevitably faces, including local optimum in the early stage, too lower convergence speed, and large complicated computation process. Sine-AGA denotes that the cross probability and mutation probability could realize the adaptive adjustments by conforming to a set of sine functions, guaranteeing to achieve a preservation scheme for optimal individuals. The whole iterative process consists of path coding, choices of fitness function, design of reproduction, crossover and mutation operations, and the setting of initial parameters of AGA. Simulation under the same condition indicates that the convergence performances of average solution and optimal solution are highly enhanced, better than the ones obtained through the pure AGA scheme, and the convergence speed is proved to be increased as expected.

The linear motor feeding system is a typical electromechanical coupling system. Conventional characteristic analyses of electromechanical coupling often overlook the influence of flexible deformation in critical components of the linear motor feeding system. Moreover, when employing genetic algorithms to optimize servo system PID control parameters, slow convergence, nonconvergence, or premature convergence problems may arise. To address these issues, this paper proposes a new performance optimization method for a linear motor feeding system. The method uses a combination of “multi-body theory + finite element” to accurately account for the flexible deformation of critical components of the feeding system, establishes a rigid–flexible electromechanical coupling model of the linear motor feeding system, and optimizes the PID parameters of the established model with an improved adaptive genetic algorithm. Simulation results demonstrate that, when utilizing an adaptive genetic algorithm to optimize the rigid–flexible electromechanical coupling model and a control system model that disregards flexible body deformation, the system achieves stability in 0.02 s and 0.027 s with overshoots of 13% and 27%, respectively. These outcomes confirm the accuracy and importance of considering flexible body deformation in the optimization performance of a linear motor feeding system. At the same time, the time required to reach the steady state of the rigid–flexible electromechanical coupling model optimized by the adaptive genetic algorithm is shortened from 0.035 s to 0.02 s. The sinusoidal signal response curve of the optimized system does not exhibit any peak overshoot compared with that of the nonoptimized system, and the response speed is also faster. These results demonstrate the effectiveness of the rigid–flexible electromechanical coupling model optimized by the nonlinear adaptive genetic algorithm. The displacement response curves of the linear motor feeding system under different workbench loads are obtained through experiments and compared with those obtained from simulations to verify the established model and the correctness of the proposed method.

Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces the lowest and surprising result compared to the existing algorithm

An adaptive genetic algorithm using mutation matrix is introduced for the solution of a series of zero/one knapsack problems of increasing complexity and structure. The evolution of the population in our adaptive genetic algorithm is based on a time dependent mutation matrix that is co-evolving, guided by the locus statistics and the fitness distribution of the population. This co-evolution of the mutation matrix provides a parameter free framework for the adaptive genetic algorithm, but different implementations of the mutation matrix have different performance for the knapsack problem. Here three structures of the knapsack problems are used as the benchmark tests: simple knapsack, parallel knapsack, and hierarchical or layer knapsack. For each type of knapsack structure, an index of difficulty is constructed based on the number of items and constraints. Numerical experiments are performed to test three models of implementation of the mutation matrix corresponding to three different way of controlling the number of mutation operations used. The numerical results for the three different knapsack problems using these three different implementations are discussed, along with a heuristic explanation for their different efficiencies. The conclusion is that an adaptive mutation matrix is best, where adaptation is based on the fitness distribution of the population, so that the mutation probability is implicitly time dependent. Furthermore, a directed mutation greatly improves the performance of all three models of implementation as this involves the imitation of the best chromosome by the less fit ones.

An adaptive genetic algorithm is presented as an intelligent algorithm for the assembly line balancing in this paper. The probability of crossover and mutation is dynamically adjusted according to the individual’s fitness value. The individuals with higher fitness values are assigned to lower probabilities of genetic operator, and vice versa. Compared with the traditional heuristic algorithms, the adaptive genetic algorithm has effective convergence and efficient computation speed. The computational results demonstrate that the proposed adaptive genetic algorithm is an effective algorithm to deal with the assembly line balancing to obtain a smoother line.

The effects of adaptive genetic algorithms (AGAs) and defected ground structures (DGSs) on performance optimization of tapered microstrip filter are investigated. The proposed structure achieves an ultra wide stopband with high attenuation within a small surface area, as well as 45% smaller size, in comparison with conventional filters. The parameters of the filter are optimized using in-home AGA code. In the proposed AGA algorithm, the crossover and mutation probabilities are adaptively changed according to the value of individual fitness. Then by utilizing the proposed DGS, a compact S-band lowpass filter with ultra-wide spurious free window is obtained. The proposed filter achieves an insertion loss of 0.8 dB from DC up to 4 GHz and 21 dB rejection in the stopband from 4.3 up to 60 GHz. The fabricated and measured results exhibit good agreement with the simulated results. They demonstrate that combining AGA and DGS yields best possible response for this group of filters.