جستجوی مقالات مرتبط با کلیدواژه "metaheuristic algorithms" در نشریات گروه "عمران"

Today, one of the important issues in the industry is the failure of parts due to the presence of holes or cracks. Among the numerical calculation tools, the classical and extended finite element method is known as the most useful numerical tools in solving engineering science problems. Identifying and investigating the types of cracks, flaws and cavities in structures is one of the most challenging issues in the field of engineering. In this article, the crack detection of two-dimensional (2D) structures using the extended finite element method (XFEM) along with genetic algorithm(GA) and grey wolf optimization method (GWO) to detect the existing crack and flaws by minimizing an error function which is also called as objective function that the evaluation of it, is based on difference between sensor measurements and suggested structure responses in each try of the algorithm.  Damage detecting in 2D domains, as a non-destructive evaluation problem, is investigated using the extended finite element method along with the optimization method of genetic algorithm and grey wolf. The extended finite element method has been used to model the structure containing cracks and holes in the abaqus program, and genetic optimization and grey wolf method have been used to determine the location of the damage in which the codes were in matlab program. The extended finite element method is a powerful tool for the analysis of structures containing cracks without remeshing and is therefore suitable for an iterative process in structural analysis. Also, in these problems, due to the wide range of parameters, it is not logical and rational to use mathematical methods. For this reason, meta heuristic methods have been developed, and grey wolf optimization methods and genetic algorithm are among these common non-gradient methods that are suitable for solving the inverse problem. This problem is set so that the optimizer algorithm finds the existing crack coordinates or holes coordinates by minimizing an objective function based on the values measured by the sensors installed on the structure. Among the limitations of the classical finite element method in the investigation of various problems in the field of fault and crack detection, we can point out the dependence of the crack or cavity on the finite element mesh, re-meshing and in other special cases the use of singular elements, which are completely removed by using The extended finite element. In this research, in order to identify the damage, the genetic optimization algorithm and the gray wolf have been used. These algorithms are designed in such a way to determine the characteristics of the damage by minimizing an error function. The defined error function is defined as the difference between the response obtained from the algorithm analysis and the response recorded in the main structure modeled in ABAQUS software, at the location of the sensors. Finally, three reference numerical examples have been solved to evaluate the capability and accuracy of the proposed method, and the result of the results shows a reduction in the cost of solving and an increase in the accuracy of the results.

One of the essential functional failures in concrete pavements is faulting. Predicting this failure can be used in various fields such as pavement design and pavement management systems. In this study, powerful tool of artificial neural networks has been used to predict this failure. Initially, using 32 input variables including traffic, weather and structural data, the artificial neural network architecture was determined by trial and error and then the specified architecture was properly trained. Among these 32 variables, in addition to the variables used in previous studies, new input variables that have not been studied so far, such as Poisson's ratio and elastic modulus of concrete slabs, have been considered. Then, with a new method, 19 important variables were identified and a new neural network model with 19 variables was constructed. The values of correlation coefficient, mean square error and mean absolute error for the model with 32 variables and 19 variables are equal to 0.97, 0.45, 0.43, 0.95, 0.54 and 0.6. Finally, using the random forest method, the importance of 19 variables was determined, of which the four most important variables are the annual cumulative number of days with precipitation greater than 12.7 mm (24%), elastic modulus (14%), Pavement life (12%) and base thickness (10%). elastic modulus is one of the most important input variables if this variable has not been studied in previous studies.

Research projects show that the desire for intelligent approaches to decision-making at various stages of the construction industry is increasing. Site layout planning is one of the important decision-making processes in the early stages of construction projects, where the location of facilities must be determined within the site. Tower crane is considered as one of the vital and expensive facilities in construction sites. Proper locating of tower crane has a significant impact on the quality, productivity, safety, cost and time of the project. In choosing the location of the tower crane, there are several criteria, including the largest lifting radius and capacity of the tower crane, the type of soil on site, the soil-bearing capacity and the material supply points. Therefore, due to the influence of many factors, tower crane planning is a complex NP-hard optimization problem, which cannot be solved through exact mathematical algorithms as the number of parameters and variables increases. Therefore, it is necessary to define the problem as an optimization problem and integrate it with mathematical modeling to reach the optimal solution. Solving such problems is usually done through metaheuristic algorithms, which belong to the category of approximate algorithms. This study provides a comprehensive review on tower crane planning problem on construction sites using mathematical modeling and metaheuristic algorithms. Based on the findings of this study, research gaps are identified in this field. Therefore, suggestions for future works have been presented in order to solve the shortcomings, which can be the subject of various research articles.

In recent years, the complexity of project implementation, competitive business environment, and limited resources of organizations have shown the need to pay attention to project management in achieving project goals. Therefore, in the implementation process, employers seek to increase quality, reduce execution time, costs, and risk, which are their main goals. In this research, optimization between the components of the survival pyramid including time, cost, quality, and risk in construction projects are done on a case-by-case basis on the Qucham reservoir dam. For this purpose, six Metahioristic optimization algorithms are used, which are three classical algorithms (genetics, Tabu search, and simulated annealing) and three new algorithms (butterfly, cyclical parthenogenesis, and harris hawk). In four cases, each component of the survival pyramid is optimized separately, and finally, all four cases are examined simultaneously. Coding related to objective functions and optimization algorithms has been done in MATLAB software. The results indicate the proper performance of the genetic algorithm. Also, in optimizing the quality index, only the genetic algorithm has given the best optimal answer, and in the combined optimization, considering all the indicators simultaneously, the genetic algorithms and the Harris hawk have given the best solution.

Optimal use of materials in the construction of structures is one of the main goals in any design. Because the construction of building structures is costly for their builders, therefore, structures and buildings that are economically justifiable and appropriate and meet the requirements of the criteria are more welcome. On the other hand, maintaining the performance of structures in earthquakes plays an important role in ensuring safety and reducing earthquake damage. Optimization of frames reduces sections, stiffness, and strength of components, and as a result, the performance of these frames against earthquakes has been questioned by researchers. In this research, the performance level of optimized steel moment frames with metaheuristic algorithms has been evaluated. For this purpose, the seismic performance of five-story steel moment frames with different geometric characteristics has been optimized and seismically evaluated using Particle Swarm Optimization (PSO), Charged System Search (CSS)Ant Colony Algorithm (ACO) and Genetic Algorithm (GA). The results of studies show that the optimized frame based on the Charged System Search algorithm has lower weight and lighter sections, and the seismic behavior responses of the frames are obtained faster. In terms of performance levels, the total number of collapse plastic joints in the Particle Swarm Optimization (PSO) was higher than other methods. Therefore, this algorithm can also be proposed as a suitable proposal for the optimal design of similar frames.

In this study, four concrete types, including ordinary Portland cement concrete, fly ash concrete, slag concrete, and slag-fly ash concrete, are taken into account in order to estimate their compressive strength by two novel machine learning methods (genetic algorithm and soccer league competition algorithm), and four types of regressions (linear, 2nd order polynomial, exponential, and logarithmic). Subsequently, the precision of prediction models are compared based on performance indicators, and the most accurate models are applied in the optimization problem modeling. Drawing on results, the most precise model to estimate the compressive strength of ordinary Portland cement concrete is the genetic algorithm, and the soccer league competition is the most accurate model to estimate the strength of other concrete types. Afterward, a model is developed so as to design mixture proportions of 40MPa concretes. Fly ash concrete, slag-fly ash concrete, and slag concrete reduce the unit cost by 35.2%, 29.9%, and 23.1%, respectively, compared with ordinary Portland cement concrete. Fly ash concrete, slag-fly ash concrete, slag concrete, and ordinary Portland cement concrete require 217.25 kg, 150.47 kg, 102 kg, and 414.64 kg cement to be manufactured. Furthermore, the slag concrete can reduce the amount of cement in the mixture proportion by 75.4%, and it is the most eco-friendly concrete.