International Journal of Optimization in Civil Engineering
Volume:14 Issue: 2, Spring 2024

During the process of continuum topology optimization some pattern discontinuities may arise. It is an important challenge to overcome such irregularities in order to achieve or interpret the true optimal layout. The present work offers an efficient algorithm based on graph theoretical approach regarding density priorities. The developed method can recognize and handle solid continuous regions in a pre-optimized media. An illustrative example shows how the proposed priority guided trees can successfully distinguish the most crucial parts of the continuum during topology optimization.

The main objective of this paper is to optimize the size and layout of planar truss structures simultaneously. To deal with this challenging type of truss optimization problem, the center of mass optimization (CMO) metaheuristic algorithm is utilized, and an extensive parametric study is conducted to find the best setting of internal parameters of the algorithm. The CMO metaheuristic is based on the physical concept of the center of mass in space. The effectiveness of the CMO metaheuristic is demonstrated through the presentation of three benchmark truss layout optimization problems. The numerical results indicate that the CMO is competitive with other metaheuristics and, in some cases, outperforms them.

Genetic algorithm is a robust meta-heuristic algorithm inspired by the theory of natural selection to solve various optimization problems. This study presents a method with the purpose of promoting the exploration and exploitation of genetic algorithm. Improvement in exploration ability is made by adjusting the initial population and adding a group of fixed stations. This modification increases the diversity among the solution population, which enables the algorithm to escape from local optimum and to converge to the global optimum even in fewer generations. On the other hand, to enhance the exploitation ability, increasing the number of selected parents is suggested and a corresponding crossover technique has been presented. In the proposed technique, the number of parents to generate offspring is variable during the process and it could be potentially more than two. The effectiveness of the modifications in the proposed method has been verified by examining several benchmark functions and engineering design problems.

In this study, the support vector machine and Monte Carlo simulation are applied to predict natural frequencies of truss structures with uncertainties. Material and geometrical properties (e.g., elasticity modulus and cross-section area) of the structure are assumed to be random variables. Thus, the effects of multiple random variables on natural frequencies are investigated. Monte Carlo simulation is used for probabilistic eigenvalue analysis of the structure. In order to reduce the computational cost of Monte Carlo simulation, a support vector machine model is trained to predict the required natural frequencies of the structure computed in the simulations. The provided examples demonstrate the computational efficiency and accuracy of the proposed method compared to the direct Monte Carlo simulation in the computation of the natural frequencies for trusses with random parameters.

The global population growth and the subsequent surge in housing demand have inevitably led to an increase in the demand for concrete, and consequently, cement. This has posed environmental challenges, as cement factories are significant contributors to carbon dioxide emissions. One promising solution is to incorporate pozzolanic materials into concrete production. This study investigates the effects of using travertine sludge as a partial substitute for cement. Seven different mix designs, along with a control mix, were created and compared. The primary variable was the ratio of travertine sludge to cement weight, considered in intervals of 10%, 15%, 20%, 25%, 30%, 35%, and 40% of the cement's weight. Various tests were conducted, including compressive strength and flexural strength at ages of 7, 28, and 90 days, as well as a permeability test at 28 days. The findings revealed interesting patterns. At the 7-day mark, as the percentage of travertine sludge increased, there was a decrease in compressive strength. However, by the 28-day mark, the concrete displayed a varied behavior: using up to 30% travertine sludge by weight reduced the strength, but exceeding 30% resulted in increased strength. At the 90-day mark, an overall increase in strength was observed with the rise in travertine sludge percentage. Such pozzolanic effects on compressive strength were somewhat predictable. Additionally, based on the flexural strength tests, travertine sludge can be deemed a viable substitute for a certain percentage of cement by weight. This research underscores the potential of sustainable alternatives in the construction industry, promoting both professional development and personal branding for those engaged in eco-friendly practices.

Seismic vibration control refers to a range of technical methods designed to reduce the effects of earthquakes on building structures and many other engineering systems. Most of the recently developed methods in this area have been investigated in vibration suppression of buildings structures each of which have advantages and disadvantages in dealing with complex structural systems and destructive earthquakes. This study aims to implement two of the well-known passive control systems as Base Isolation (BI) and Mass Damper (MD) control as a hybrid control scheme in order to reduce the seismic vibration of tall tubular buildings in dealing with different types of earthquakes. For this purpose, a 50-story tall building is considered with tubular structural system while the hybrid BI-MD control system ins implemented in the building for vibration control purposes. Since the parameter tuning process is one of the key aspects of the passive control systems, a metaheuristic-based parameter optimization process is conducted for this purpose in which a new upgraded version of the standard Gazelle Optimization Algorithm (GOA) is proposed as UGOA while the Chaos Theory (CT) is used instead of random movements in the main search loop of the UGOA in order to enhance the overall performance of the standard algorithm. The results show that the upgraded algorithm is capable of conducting better search in dealing with the optimal hybrid control of structural systems.

The main objective of this study is to predict the maximum inter-story drift ratios of steel moment-resisting frame (MRF) structures at different seismic performance levels using feed-forward back-propagation (FFBP) neural network models. FFBP neural network models with varying numbers of hidden layer neurons (5, 10, 15, 20, and 50) were trained to predict the maximum inter-story drift ratios of 5- and 10-story steel MRF structures. The numerical simulations indicate that FFBP neural network models with ten hidden layer neurons better predict the inter-story drift ratios at seismic performance levels for both 5- and 10-story steel MRFs compared to other neural network models.

Most industrial-practical projects deal with nonlinearity phenomena. Therefore, it is vital to implement a nonlinear method to analyze their behavior. The Finite Element Method (FEM) is one of the most powerful and popular numerical methods for either linear or nonlinear analysis. Although this method is absolutely robust, it suffers from some drawbacks. One of them is convergency issues, especially in large deformation problems. Prevalent iterative methods such as the Newton-Raphson algorithm and its various modified versions cannot converge in certain problems including some cases such as snap-back or through-back. There are some appropriate methods to overcome this issue such as the arc-length method. However, these methods are difficult to implement. In this paper, a computational framework is presented based on meta-heuristic algorithms to improve nonlinear finite element analysis, especially in large deformation problems. The proposed method is verified via different benchmark problems solved by commercial software. Finally, the robustness of the proposed algorithm is discussed compared to the classic methods.