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Reliable and accurate streamflow forecasting plays a crucial role in water resources systems (WRS) especially in dams operation and watershed management. However, due to the high uncertainty associated WRS components and nonlinear nature of streamflow generations, the realistic streamflow forecasts is still one of the most challenging issue in WRS. This paper aimed to forecast one-month ahead streamflow of Karun river (Iran) by coupling an artificial neural network (ANN) with an improved binary version of gravitational search algorithm (IBGSA), named ANN- IBGSA. To this end, the best lag number for each predictor at Poleshaloo station was firstly selected by auto-correlation function (ACF). The ANN-IBGSA was used to minimize the sum of RMSE and R2 and to identify the optimal predictors. Finally, to characterize the hydro-climatic uncertainties associated with the selected predictors, an
implicit approach of Monte-Carlo simulation (MCS) was applied. The ACF plots indicated a significant correlation up to a lag of two months for the input predictors. The ANN-IBGSA identified the Tmean (t-1), Q(t-1) and Q(t) as the best predictors. Findings demonstrated that the ANN-IBGSA forecasts were considerably better than those previously carried out by researchers in 2013. The average improvement values were 9.91%, 11.85% and 9.13% for RMSE, R2 and MAE, respectively. The Monte-Carlo simulations demonstrated that all of forecasted values lie within the 95% confidence intervals.
 

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In recent years many researchers prefer to use metaheuristic algorithms to reach the optimum design of structures. In this study, an Enhanced Vibrating Particle System (EVPS) is applied to get the minimum weight of large-scale dome trusses under frequency constraints. Vibration frequencies are important parameters, which can be used to control the responses of a structure that is subjected to dynamic excitation. The truss structures were analyzed by finite element method and optimization processes were implemented by the computer program coded in MATLAB. The effectiveness and efficiency of the Enhanced Vibrating Particle System (EVPS) is investigated in three large-scale dome trusses 600-, 1180-, and 1410-bar to obtain the weight optimization with frequency constraints.

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Depending on the tectonic activities, most buildings subject to multiple earthquakes, while a single design earthquake is suggested in most seismic design codes. Perhaps, the lack of easy assessment to second shock information and sometimes use of inappropriate methods in estimating these features cause successive earthquakes mainly were ignored in the analysis procedure. In order to overcome to above deficiencies, the learning abilities of artificial neural networks (ANNs) are used in two steps to evaluate the seismic capacity of steel frames consisting moment-resisting frames, ordinary concentrically, and buckling restrained brace (BRB) under critical consecutive earthquakes. For this purpose, peak ground acceleration of second shock (PGA_a) is estimated based on the first shock features in the first step. Next, second ANNs estimate the decreased capacity of the damaged structure for LS and CP performance level according to the proposed PGA_a from the previous step and some seismic and structural features. The results indicate that ANNs are trained to generalize the unseen information very well and reflect good precision in predicting target results in both steps. Finally, the effect of different parameters and repeated shocks is investigated on the seismic performance of mentioned frames. The results show the proper performance of BRB frames in the case of real and repeated earthquakes.
 

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In this study the challenges of managing the civil projects in oil and gas industry over recent years that failed were investigated. For this purpose, the relevant cases and their effectiveness were categorized by analyzing research data obtained from the questionnaire results. The results obtained from the research showed that there is a positive and significant relationship between the project management knowledge and reduction in the challenges. Lack of attention to the project's feasibility study before starting the project, adverse risks at the beginning and end of the projects, proper knowledge of contracts, and the project team's skill are the items that will fail the project if they are not appropriately managed. Since the team's correct design and the key persons of the project and before that feasibility and the necessity of doing it in vital projects in the country are very important and in such a way, the two components studied in this research are derived from the risk management of projects. Considering the importance of this issue as a case study, these cases were investigated in gas pipeline projects in Fars province.
 

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The cycle basis of a graph arises in a wide range of engineering problems and has a variety of applications. Minimal and optimal cycle bases reduce the time and memory required for most of such applications. One of the important applications of cycle basis in civil engineering is its use in the force method to frame analysis to generate sparse flexibility matrices, which is needed for optimal analysis.
In this paper, the simulated annealing algorithm has been employed to form suboptimal cycle basis. The simulated annealing algorithm works by using local search generating neighbor solution, and also escapes local optima by accepting worse solutions. The results show that this algorithm can be used to generate suboptimal and subminimal cycle bases. Compared to the existing heuristic algorithms, it provides better results. One of the advantages of this algorithm is its simplicity and its ease for implementation.
 

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Today, due to the complexity of engineering problems and at the same time the advancement of computer science, the use of machine learning (ML) methods and soft computing methods in solving engineering problems has been considered by many researchers. These methods can be used to find accurate estimates for problems in various scientific fields. This paper investigates the effectiveness of the Adaptive Network-Based Fuzzy Inference System (ANFIS) hybridized with Teaching Learning Based Optimization Algorithm (TLBO), to predict the ultimate strength of columns with square and rectangular cross-sections, confide with various fiber-reinforced polymer (FRP) sheets. In previous studies by many researchers, several experiments have been conducted on concrete columns confined by FRP sheets. The results indicate that FRP sheets effectively increase the compressive strength of concrete columns. Comparing the results of ANFIS-TLBO with the experimental findings, which were agreeably consistent, demonstrated the ability of ANFIS-TLBO to estimate the compressive strength of concrete confined by FRP. Also, the comparison of RMSE, SD, and R² for ANFIS-TLBO and the studies of different researchers show that the ANFIS-TLBO approach has a good performance in estimating compressive strength. For example, the value of R² in the proposed method was 0.92, while this parameter was 0.87 at best among the previous studies. Also, the obtained error in the prediction of the proposed model is much lower than the obtained error in the previous studies. Hence, the proposed model is more efficient and works better than other techniques.
 

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The lateral-torsional buckling (LTB) strength of cellular steel girders that were subjected to web distortion was rarely examined. Since no formulation has been presented for predicting the capacity of such beams, in the current paper an extensive numerical investigation containing 660 specimens was modeled using finite element analysis (FEA) to consider the ultimate lateral-distortional buckling (LDB) strength of such members. Then, a reliable algorithm based on the artificial neural networks (ANNs) was developed and the most accurate model was chosen to derive an efficient formula to evaluate the LDB capacity of steel cellular beams. The input and target data required in the ANN models were provided using the ANN analyzes. An attempt was made to include the proposed formula in all the variables affecting the LDB of cellular steel beams. In the next step, the validity of the proposed formula was proved by several statistical criteria, and also the most influential input variable was discussed. eventually, a comparison study was executed between the results provided by the ANN-based equation and the AS4100, EC3, and AISC codes. It was revealed that the presented equation is accurate enough and can be used by practical engineers.
 

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In this study, the finite element model updating was simulated by reducing the stiffness of the members. Due to lack of access to the experimental results, the data obtained from an analytical model were used in the proposed structural damage scenarios. The updating parameters for the studied structures were defined as a reduction coefficient applied to the stiffness of the members. Parameter variations were calculated by solving an unconstrained nonlinear optimization problem. The objective function in the optimization problem was proposed based on the Multi-Degree-of-Freedom (MDOF) equations of motion as well as the dynamic characteristics of the studied structure. Only the first natural frequency of the damaged structure was used in the proposed updating process, and only one vibration mode was used in the updating problem and damage identification procedure. In addition, as elimination of high-order terms in the proposed formula introduced errors in the final response, the variations of natural frequency and vibration mode for higher-order terms were included in the free vibration equation of the proposed objective function. The Colliding Bodies Optimization (CBO) algorithm was used to solve the optimization problem. The performance of the proposed method was evaluated using the numerical examples, where different conditions were applied to the studied structures. The results of the present study showed that, the proposed method and formulation were capable of efficiently updating the dynamic parameters of the structure as well as identifying the location and severity of the damage using only the first natural frequency of the structure.
 

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This paper presents an efficient wavelet-based genetic algorithm strategy for optimal sensorexciter placement (OSPOEP) in large-scaled structures suitable for time-domain structural identification. For this purpose, a wavelet-based scheme is introduced in order to improve the fitness evaluation of GA-based individuals capable of using adaptive wavelets. A search domain reduction (SDR) strategy is proposed to reduce the wide space of initial unknowns corresponding to enormous degrees-of-freedom in large systems. The proposed reduction strategy is carried out at three stages according to the use of different wavelet functions. Furthermore, a multi-species decimal GA coding system is modified for a competent search around the local optima. In this regards, a local operation of mutation is presented in addition with regeneration and reintroduction operators. It is deduced that, the reliable OSPOEP strategy prior to the time-domain identification will be achieved by those procedures dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the excitation effects. The numerical assessment on the appropriateness and capability of the proposed approach demonstrates the substantially high computational performance and fast convergence of the proposed OSPOEP strategy, especially in large-scaled structural systems. It is concluded that, the robustness of the proposed OSPOEP procedure lies on the precise and fast fitness evaluation at larger sampling rates which resulting in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution.
 

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Beyond common practice that treats structural damage detection as an optimization problem, the present work offers another approach that updates boundaries of the damage ratios. In this approach the bandwidth between such lower and upper boundaries, is adaptively reduced aiming to coincide at the true damage state. Formulation of the proposed method is developed using modal strain energy in a system of finite elements. A resolution-based technique is applied so that the search space cardinality can be defined and then reduced. The proposed method is validated on different structural types including beam, frame and truss examples with various damage scenarios. The results exhibit high cardinality reduction and capability of the proposed iterative method in squeezing the design space for more efficient search.
 

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Explosions are inevitable in today’s world; therefore, building structures may be dynamically loaded by an intense loading during the explosion. This is why regulatory bodies have provided instructions for determining the response of structures under the explosion load. Previous research has shown that when the explosion happens close to a structure, the ground explosion load can be modeled as tensile and compressive loads. This research investigates the response of an elastic-plastic single-degree-of-freedom system subjected to different explosive loads with different positive durations. The maximum intensity of blast load and blast duration remains constant, and the positive phase duration is the only variable that changes. The nonlinear dynamic responses of a single-degree-of-freedom system (i.e., displacement, velocity, acceleration, and ductility) are calculated using the linear acceleration method. The results show that increasing the positive phase duration and the amount of positive impact can increase the maximum displacement and ductility of the system. Also, it can be concluded that the maximum acceleration of the studied systems remains constant when the values for the blast impact and positive phase durations change.
 

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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.
 

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Despite the advantages of the plastic limit analysis of structures, this robust method suffers from some drawbacks such as intense computational cost. Through two recent decades, metaheuristic algorithms have improved the performance of plastic limit analysis, especially in structural problems. Additionally, graph theoretical algorithms have decreased the computational time of the process impressively. However, the iterative procedure and its relative computational memory and time have remained a challenge, up to now. In this paper, a metaheuristic-based artificial neural network (ANN), which is categorized as a supervised machine learning technique, has been employed to determine the collapse load factors of two-dimensional frames in an absolutely fast manner. The numerical examples indicate that the proposed method's performance and accuracy are satisfactory.
 

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An efficient method is proposed by using time domain responses and topology optimization to identify the location and severity of damages in two-dimensional structures under plane stress assumption. Damage is assumed in the form of material density reduction in the finite element model of the structure. The time domain responses utilized here, are the nodal accelerations measured at certain points of the structure. The responses are obtained by the Newmark method and contaminated with uniformly random noise in order to simulate real conditions. Damage indicators are extracted from the time domain responses by using Singular Value Decomposition (SVD). The problem of damage detection is presented as a topology optimization problem and the Solid Isotropic Material with Penalization (SIMP) method is used for appropriate damage modeling. The objective function is formed based on the difference of singular values of the Hankel matrix for responses of real structure and the analytical model. In order to evaluate the correctness of the proposed method, some numerical examples are examined. The results indicate efficiency of the proposed method in structural damage detection and its parameters such as resampling length in SVD, penalty factor in the SIMP method and number and location of sensors are effective parameters for improving the results.

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In this article, topology optimization of two-dimensional (2D) building frames subjected to seismic loading is performed using the polygonal finite element method. Artificial ground motion accelerograms compatible with the design response spectrum of ASCE 7-16 are generated for the response history dynamic analysis needed in the optimization. The mean compliance of structure is minimized as a typical objective function under the material volume fraction constraint. Also, the adjoint method is employed for the sensitivity analysis evaluated in terms of spatial and time discretization. The ground structures are 2D continua taking the main structural components (columns and beams) as passive regions (solid) to render planar frames with additional components. Hence, building frames with different aspect ratios are considered to assess the usefulness of the additional structural components when applying the earthquake ground motions. Furthermore, final results are obtained for different ground motions to investigate the effects of ground motion variability on the optimized topologies.
 

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In this article, spectral matching of ground motions is presented via the Mouth Brooding Fish (MBF) algorithm that is recently developed. It is based on mouth brooding fish life cycle. This algorithm utilizes the movements of the mouth brooding fish and their children’s struggle for survival as a pattern to find the best possible answer. For this purpose, wavelet transform is used to decompose the original ground motions to several levels and then each level is multiplied by a variable. Subsequently, this algorithm is employed to determine the variables and wavelet transform modifies the recorded accelerograms until the response spectrum gets close to a specified design spectrum. The performance of this algorithm is investigated through a numerical example and also it is compared with CBO and ECBO algorithms. The numerical results indicate that the MBF algorithm can to construct very promising results and has merits in solving challenging optimization problems.
 

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Most structures are asymmetric due to functionality requirements and limitations. This study investigates the effect of asymmetry on damage detection. For this purpose, the asymmetry has been applied to models by considering different spans’ length and also different geometry properties for the section of members. Two types of structures comprising symmetric and asymmetric truss and frame have been modeled considering multiple damage scenarios and noise-contaminated data. Three objective functions based on flexibility matrix, natural frequency and modal frequency are proposed. These objective functions are optimized utilizing multiverse optimizer (MVO). For the symmetric models using limited modal data, flexibility-based objective function has the most accurate results, while by increasing the number of mode shapes, its accuracy reduced. Among asymmetric models of truss, damage detection results of the model is more accurate than those of its symmetric pair. Between asymmetric models of frame, the results obtained from frames which have only different spans’ length are more precise than those of the symmetric model. This is while frequency-based objective functions have their least accurate results for the frame model having asymmetry only in the section properties of its elements.
 

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A collision between bodies is an important phenomenon in many engineering practical applications. The most important problem with the collision analysis is determining the hysteresis damping factor or the hysteresis damping ratio. The hysteresis damping ratio is related to the coefficient of restitution in the collision between two solid bodies. In this paper, at first, the relation between the deformation and its velocity of the contact process is presented. Due to the complexity of the problem under study, a new powerful hybrid metaheuristic method is used to achieve the optimal model. For this purpose, by using imperialist competitive ant colony optimization algorithm, for minimizing the root mean square of the hysteresis damping ratio, the optimal model is determined. The optimal model is entirely acceptable for the wide range of the coefficient of restitution. So, it can be used in hard and soft impact problems.
 

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One of the important factors in the efficiency of construction operations is the proper replacement construction projects of the construction site layout planning (CSLP). That this would not be possible without oversight of the factors affecting it. Therefore, the study of factors affecting the replacement of construction site layout is considered vital in projects. Different factors are involved in the replacement of CSLP, which examine the economic dimension and the effects of changing costs and time during work. Due to the complexity of the subject, it is solved using hyper-innovative algorithms. This research is a linear programming model for optimizing the layout of equipment for Launcher/Receiver (L/R) stations. Due to the complexity of the problem, the invasive weed algorithm was used to achieve an optimal response. The goal is to minimize the total costs associated with transportation, relocation and relocation, and changes during implementation. The results of the calculations and output of the algorithm showed the variation of the answer in the optimal layout of the CSLP, which was obtained at the lowest distance and the most optimal mode. The results were presented in a similar scenario in the projects.
 

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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.