International Journal of Optimization in Civil Engineering
Volume:13 Issue: 1, Winter 2023

Design optimization of cable-stayed bridges is a challenging optimization problem because a large number of variables is usually involved in the optimization process. For these structures the design variables are cross-sectional areas of the cables. In this study, an efficient metaheuristic algorithm namely, momentum search algorithm (MSA) is used to optimize the design of cable-stayed bridges. The MSA is inspired by the Physics and its superiority over many metaheuristics has been demonstrated in tackling several standard benchmark test functions. In the current work, the performance of MSA is compared with that of two other metaheuristics and it is shown that the MSA is an efficient algorithm to tackle the optimization problem of cable-stayed bridges.

Colliding Bodies Optimization (CBO) is a population-based metaheuristic algorithm that complies physics laws of momentum and energy. Due to the stagnation susceptibility of CBO by premature convergence and falling into local optima, some meritorious methodologies based on Sine Cosine Algorithm and a mutation operator were considered to mitigate the shortcomings mentioned earlier. Sine Cosine Algorithm (SCA) is a stochastic optimization method that employs sine and cosine based mathematical models to update a randomly generated initial population. In this paper, we developed a new hybrid approach called hybrid CBO with SCA (HCBOSCA) to obtain reliable structural design optimization of discrete and continuous variable structures, where a memory was defined to intensify the convergence speed of the algorithm. Finally, three structural problems were studied and compared to some state of the art optimization methods. The experimental results confirmed the competence of the proposed algorithm.

The main aim of this study, is to evaluate the seismic reliability of steel concentrically braced frame (SCBF) structures optimally designed in the context of performance-based design. The Monte Carlo simulation (MCS) method and neural network (NN) techniques were utilized to conduct the reliability analysis of the optimally designed SCBFs. Multi-layer perceptron (MLP) trained by back propagation technique was used to evaluate the required structural responses and then the total exceedence probability associated with the seismic performance levels was estimated by the MCS method. Three numerical examples of 5-, 10-, and 15-story SCBFs with fixed and optimal topology of braces are presented and their probability of failure was evaluated considering the resistance characteristics and the seismic loading of the structures. The numerical results indicate that the SCBFs with optimal topology of braces were more reliable than those with fixed topology of braces.

This paper presents a computational framework for optimal design of non-prismatic reinforced concrete box girder bridges. The variables include the geometry of the cross section, tapered length, concrete strength and reinforcement of box girders and slabs. These are obtained by the enhanced colliding bodies optimization algorithm to optimizing the cost and again CO2 emission. Loading and design is based on the AASHTO standard specification. The methodology is illustrated by a three-span continuous bridge. The trade-off between optimal cost and CO2 emission in this type of bridge indicates that the difference of costs, as well as CO2 emissions in the solution with both objectives is less than 1%. However, the optimal variables in the cost objective are different from the variables of CO2 emission objective.

The real-world applications addressing the nonlinear functions of multiple variables could be implicitly assessed through structural reliability analysis. This study establishes an efficient algorithm for resolving highly nonlinear structural reliability problems. To this end, first a numerical nonlinear optimization algorithm with a new simple filter is defined to locate and estimate the most probable point in the standard normal space and the subsequent reliability index with a fast convergence rate. The problem is solved by using a modified trust-region sequential quadratic programming approach that evaluates step direction and tunes step size through a linearized procedure. Then, the probability expectation method is implemented to eliminate the linearization error. The new applications of the proposed method could overcome high nonlinearity of the limit state function and improve the accuracy of the final result, in good agreement with the Monte Carlo sampling results. The proposed algorithm robustness is comparatively shown in various numerical benchmark examples via well-established classes of the first-order reliability methods. The results demonstrate the successive performance of the proposed method in capturing an accurate reliability index with higher convergence rate and competitive effectiveness compared with the other first-order methods.

Remarkable growth in the use of AI in various fields of civil engineering is going on in the new era. The applications of Artificial Intelligence (AI) are widely considered for specifying the mechanical properties of concretes and noticeable results are reported. Hence, this systematic review aims to study different methods presented in various research in this regard. The gaps and shortcomings of the previous studies are presented, which can shed light on future studies by presenting new ideas. The major issues that the research seek to examine are accuracy and authenticity. The experimental costs and time spent specifying the concrete's mechanical properties will significantly reduce using AI techniques. It is recommended to employ AI methods more widely for composite materials. The suggestions presented here can be beneficial to those aiming to advance in this significant and offer more innovations.

Energy production and consumption play an important role in the domestic and international strategic decisions globally. Monitoring the electric energy consumption is essential for the short- and long-term of sustainable development planned in different countries. One of the advanced methods and/or algorithms applied in this prediction is the meta-heuristic algorithm. The meta-heuristic algorithms can minimize the errors and standard deviations in the data processing. Statistically, there are numerous methods applicable in the uncertainty analysis and in realizing the errors in the datasets, if any. In this article, the Mean Absolute Percentage Error (MAPE) is used in the error’s minimization within the relevant algorithms, and the used dataset is actually relating to the past fifty years, say from 1972 to 2021. For this purpose, the three algorithms such as the Imputation–Regularized Optimization (IRO), Colliding Bodies Optimization (CBO), and Enhanced Colliding Bodies Optimization (ECBO) have been used. Each one of the algorithms has been implemented for the two linear and exponential models. Among this combination of the six models, the linear model of the ECBO meta-heuristic algorithm has yielded the least error. The magnitude of this error is about 3.7%. The predicted energy consumption with the winning model planned for the year 2030 is about 459 terawatt-hours. The important socio-economical parameters are used in predicting the energy consumption, where these parameters include the electricity price, Gross Domestic Product (GDP), previous year's consumption, and also the population. Application of the meta-heuristic algorithms could help the electricity generation industries to calculate the energy consumption of the approaching years with the least error. Researchers should use various algorithms to minimize this error and make the more realistic prediction.

Optimization methods are essential in today's world. Several types of optimization methods exist, and deterministic methods cannot solve some problems, so approximate optimization methods are used. The use of approximate optimization methods is therefore widespread. One of the metaheuristic algorithms for optimization, the EVPS algorithm has been successfully applied to engineering problems, particularly structural engineering problems. As this algorithm requires experimental parameters, this research presents a method for determining these parameters for each problem and a self-adaptive algorithm called the SA-EVPS algorithm. In this study, the SA-EVPS algorithm is compared with the EVPS algorithm using the 72-bar spatial truss structure and three classical benchmarked functions