Publisher's Synopsis
Civil engineering is one of the oldest engineering disciplines, since civil engineers of one form or another have been around ever since humans started building major public works such as roads, bridges, tunnels, and large public buildings. It is also an incredibly broad discipline, spanning treatment of environmental issues, transportation, power generation, and major structures. Structural optimisation is one of the most challenging research topics in the field of computational mechanics. It has received more and more attention recently because of its great potential application in many industrial areas. Its importance lies in the fact that the appropriate result of structural design is generally the most decisive factor that influences the product efficiency. Meta-heuristic algorithms proved to find optimal solutions for combinatorial problems in many domains. Nevertheless, the efficiency of these algorithms highly depends on their parameter settings. In fact, finding appropriate settings of the algorithm's parameters is considered to be a non- trivial task and is usually set manually to values that are known to give reasonable performance. Metaheuristics and Optimisation in Civil Engineering address evolutionary algorithms and metaheuristics applied in solving optimum design problems in civil engineering, construction management and related topics. This timely volume deals the applications of metaheuristic algorithms, with a primary focus on optimization problems in civil engineering offering practical case studies as examples to demonstrate real world applications. It shows that the maximum stress minimization problem and the compliance minimization problem have equal optimal results of pure bending beam under a single loading condition. The main objective of this compilation is to bring together researchers and to field specialists presenting on new approaches, in the field of optimization, metaheuristics and evolutionary algorithms in civil engineering. Additionally, Ant Colony Optimization with Parametric Analysis (ACO-PA) is also proposed. In the last, it presents a description of the major contributions made by a selection of different metaheuristics, including simulated annealing, tabu search, genetic algorithms, ant colony algorithms, particle swarm algorithms, and harmony search algorithms.
