International Journal of Optimization in Civil Engineering
Volume:11 Issue: 1, Winter 2021

Economy and safety are two important components in structural design process and stablishing a balance between them indeed results in improved structural performance specially in large-scale structures including space lattice domes. Topology optimization of geometrically nonlinear single-layer lamella, network, and geodesic lattice domes is implemented using enhanced colliding-bodies optimization algorithm for three different spans and two different dead loading conditions. Collapse reliability index of these optimal designs is evaluated to assess the safety of the structures against overall collapse using Monte-Carlo simulation method. The numerical results of this study indicate that the reliability index of most of the optimally designed nonlinear lattice domes is low and this means that the safety of these structures against overall collapse is questionable.

Parallel Cellular Automata (PCA) previously has been employed for optimizing bi-objective reservoir operation, where one release is used to meet both objectives. However, if a single release can only be used for one objective, meaning two separate sets of releases are needed, the method is not applicable anymore. In this paper, Multi-Step Parallel Cellular Automata (MSPCA) has been developed for bi-objective optimization of single-reservoir systems’ operation. To this end, a novel cellular automata formulation is proposed for such problems so that PCA’s incapability when dealing with them will be overcome. In order to determine all releases throughout the operation period, in each iteration – unlike PCA – two updates take place so as to calculate releases individually. Since a bi-objective problem in Dez reservoir (in southern Iran) has been solved by PCA in earlier works, the same data is used here. The results are given for a 60-months operation period, and to evaluate this method, the results of Non-Dominated Sorting Genetic Algorithm (NSGAII) is also given for the same problem. The comparison shows MSPCA, beside remarkable reduction in computational costs, gives up solutions with higher quality as well.

In this article, an Advanced Charged System Search (ACSS) algorithm is applied for the optimum design of steel structures. ACSS uses the idea of Opposition-based Learning and Levy flight to enhance the optimization abilities of the standard CSS. It also utilizes the information of the position of each charged particle in the subsequent search process to increase the convergence speed. The objective function is to find a minimum weight by choosing suitable sections subjected to strength and displacement requirements specified by the American Institute of Steel Construction (AISC) standard subject to the loads defined by Load Resistance Factor Design (LRFD). To show the performance of the ACSS, four steel structures with different number of elements are optimized. The results, efficiency, and accuracy of the ACSS algorithm are compared to other meta-heuristic algorithms. The results show the superiority of the ACSS compared to the other considered algorithms.

Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired by the theory of the universe evolution and Artificial Physics Optimization (APO) which is a physical base optimization method. Finally, the performance of the proposed optimization method is compared with the originated methods. Moreover, the performance of the proposed algorithm is evaluated for truss optimization as an applied constrained optimization problem.

The capacity vehicle routing problem (CVRP) is one of the most famous issues in combinatorial optimization that has been considered so far, and has attracted the attention of many scientists and researchers today. Therefore, many exact, heuristic and meta-heuristic methods have been presented in recent decades to solve it. In this paper, due to the weaknesses in the particle swarm optimization (PSO), a hybrid-modified version of this algorithm called PPSO is presented to solve the CVRP problem. In order to evaluate the efficiency of the algorithm, 14 standard examples from 50 to 199 customers of the existing literature were considered and the results were compared with other meta-heuristic algorithms. The results show that the proposed algorithm is competitive with other meta-heuristic algorithms. Besides, this algorithm obtained very close answers to the best known solutions (BKSs) for most of the examples, so that the seven BKSs were produced by PPSO.

Mechanical excavators are widely utilized in civil/mining engineering projects. There are several types of mechanical excavators, such as an impact hammer, tunnel boring machine (TBM) and roadheader. Among these, roadheaders have some advantages (such as, initial investment cost, elimination of blast vibration, minimal ground disturbances and reduced ventilation requirements). The poor performance estimation of the roadheaders can lead to costly contractual claims. Relevance vector regression (RVR) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of RVR in prediction of roadheader performance. The estimation abilities offered using RVR was presented by using field data of achieved from tunnels for Istanbul’s sewerage system, Turkey. In this model, Schmidt hammer rebound values and rock quality designation (RQD) were utilized as the input parameters, while net cutting rates was the output parameter. As statistical indices, coefficient of determination (R2) and mean square error (MSE) were used to evaluate the efficiency of the RVR model. According to the obtained results, it was observed that RVR model can effectively be implemented for roadheader performance prediction.

The artificial neural network is such a model of biological neural networks containing some of their characteristics and being a member of intelligent dynamic systems. The purpose of applying ANN in civil engineering is their efficiency in some problems that do not have a specific solution or their solution would be very time-consuming. In this study, four different neural networks including FeedForward BackPropagation (FFBP), Radial Basis Function (RBF), Extended Radial Basis Function (ERBF), and Generalized Regression Neural Network (GRNN) have been efficiently trained to analyze large-scale space structures specifically double-layer barrel vaults focusing on their maximum element stresses. To investigate the efficiency of the neural networks, an example has been done and their corresponding results have been compared with their exact amounts obtained by the numerical solution.

In the last fifty years the climate change has become an important problem with high social and economic impact. Sadly, there are plenty of events that evidence the risks that the climate-change carries on our own lives, but also on our built environment. One of the most important and oldest building materials used by humans is the timber. Being a natural material it has a direct interaction with the climate factors, therefore it is impacted by the phenomenon of the climate change, as well. Besides other characteristics, the moisture content of the wooden cells is one of the most challenged properties by the global warming. It is a basic requirement that all wood products are made from raw materials with a moisture content that is the expected equilibrium wood moisture at the point of use, otherwise the finished product may be damaged due to greater swelling or shrinkage, pronounced deformation and cracking, making it unsuitable for its intended use. Thus timber buildings older than forty-fifty years, witness to the global warming can be seriously affected by changes in characteristics like strength, stiffness, hardness, high deformation values or appearance of biologically active compounds. In order to protect these structures an understanding of the nature of these changes and setup a series of methods is necessary, without damaging the cultural heritage sites. The aim of the present review is to summarize the impact of the environment, climate and climate-change on timber buildings, and to present the most important analytical methods from the literature, used for the study of wooden material.