---

The excavation damaged zone (EDZ) can be defined as a rock zone where the rock properties and conditions have been changed due to the processes related to an excavation. This zone affects the behavior of rock mass surrounding the construction that reduces the stability and safety factor and increase probability of failure of the structure. In this paper, a methodology was examined for computing the creation probability of damaged zone by Latin hypercube sampling based on a feed-forward artificial neural network (ANN) optimized by hybrid particle swarm optimization and genetic algorithm (HPSOGA). The HPSOGA was carried out to decide the initial weights of the neural network. A case study in a test gallery of the Gotvand dam, Iran was carried out and creation probabilities of 0.191 for highly damaged zone (HDZ) and 0.502 for EDZ were obtained.

---

A charged system search algorithm (CSS) is applied to the optimal cost design of water distribution networks. This algorithm is inspired by the Coulomb and Gauss’s laws of electrostatics in physics. The CSS utilizes a number of charged particles which influence each other based on their fitness values and their separation distances considering the governing law of Coulomb. The well-known benchmark instances, Hanoi network, double Hanoi network, and New York City tunnel problem, are utilized as the case studies to evaluate the optimization performance of CSS. Comparison of the results of the CSS with some other meta-heuristic algorithms indicates the performance of the new algorithm.

---

The Hartley transform, a real-valued alternative to the complex Fourier transform, is presented as an efficient tool for the analysis and simulation of earthquake accelerograms. This paper is introduced a novel method based on discrete Hartley transform (DHT) and radial basis function (RBF) neural network for generation of artificial earthquake accelerograms from specific target spectrums. Acceleration time histories of horizontal earthquake ground motion are obtained by the capability of learning of RBF neural network to expand the knowledge of the inverse mapping from the response spectrum to earthquake accelerogram. In the first step, Hartley transform is used to decompose earthquake accelerograms, then a RBF neural network is trained to learn to relate the response spectrum to Hartley spectrum. Finally, the generated accelerogram using inverse discrete Hartley transform is obtained from target spectrum. Approximately 200 uniformly scaled horizontal ground motion records from recent Iran’s earthquakes are used to decompose with real Hartley transform and train networks.

---

Structural vibration control is one of the most important features in structural engineering. Real-time information about seismic resultant forces is required for deciding module of intelligent control systems. Evaluation of lateral forces during an earthquake is a complicated problem considering uncertainties of gravity loads amount and distribution and earthquake characteristics. An artificial neural network (ANN) has been trained in this article to estimate these forces. This ANN was trained on the results of time history analysis of a three-story building under 702 different loadings. Results of numerical examples verify that the trained ANN can predict the expected forces with negligible deviations.

---

This paper is concerned with the determination of optimal sensor locations for structural modal identification in a strap-braced cold formed steel frame based on an improved genetic algorithm (IGA). Six different optimal sensor placement performance indices have been taken as the fitness functions two based on modal assurance criterion (MAC), two based on maximization of the determinant of a Fisher information matrix (FIM), one aim on the maximization of the modal energy and the last is a combination of two aforementioned indices. The decimal two-dimension array coding method instead of binary coding method is applied to code the solution. Forced mutation operator is applied whenever the identical genes produce via the crossover procedure. An improvement is also introduced to mutation operator of the IGA. A verified computational simulation of a strap-braced cold formed steel frame model has been implemented to demonstrate the effectiveness and application of the proposed method. The obtained optimal sensor placements using IGA are compared with those gained by the conventional methods based on several criteria such as norms of FIM and minimum in off-diagonal terms of MAC. The results showed that the proposed IGA can provide sensor locations as well as the conventional methods. More important, based on the criteria, four of the six fitness functions, can identify the vibration characteristics of the frame model accurately. It is shown through the example that in comparison with the MAC-based performance indices, the use of the FIM-based fitness functions results in more acceptable and reasonable configurations.

---

In this paper simple formulae are derived for calculating the number of spanning trees of different product graphs. The products considered in here consists of Cartesian, strong Cartesian, direct, Lexicographic and double graph. For this purpose, the Laplacian matrices of these product graphs are used. Form some of these products simple formulae are derived and whenever direct formulation was not possible, first their Laplacian matrices are transformed into single block diagonal forms and then using the concept of determinant, the calculations are performed.

---

In this research, optimal design and assessment of multiple tuned mass dampers (MTMDs) capability in mitigating the damage of nonlinear steel structures subjected to earthquake excitation has been studied. Optimal parameters of TMDs on nonlinear multi-degree-of-freedom (MDOF) structures have been determined based on minimizing the maximum relative displacement (drift) of structure where for solving the optimization problem the genetic algorithm (GA) has been used successfully. For numerical analysis, three and nine storey 2-D moment resisting nonlinear steel frames subjected to far-field and near-field earthquakes and optimal MTMDs has been designed for different values of mass ratio and TMDs number. According to the results of numerical simulations, it can be said that MTMDs mechanism could reduce the damage of nonlinear steel structures where the effectiveness increases by increasing TMDs mass ratio. Also the performance of MTMDs depends on earthquake characteristics, mass ratio and TMDs configuration where in this research the effective case has been locating TMDs on top floor in parallel configuration.

---

A new method for structural topology optimization is introduced which employs the Isogeometric Analysis (IA) method. In this approach, an implicit function is constructed over the whole domain by Non-Uniform Rational B-Spline (NURBS) basis functions which are also used for creating the geometry and the surface of solution of the elasticity problem. Inspiration of the level set method zero level of the function describes the boundary of the structure. An optimality criterion is derived to improve the implicit function towards the optimum boundaries. The last section of this paper is devoted to some numerical examples in order to demonstrate the performance of the method as well as the concluding remarks.

---

This paper describes the application of the recently developed metaheuristic algorithm for simultaneous analysis, design and optimization of Water Distribution Systems (WDSs). In this method, analysis is carried out using Colliding Bodies Optimization algorithm (CBO). The CBO is a population-based search approach that imitates nature’s ongoing search for better solutions. Also, design and cost optimization of WDSs are performed simultaneous with analysis process using a new objective function in order to satisfying the analysis criteria, design constraints and cost optimization. A number of practical examples of WDSs are selected to demonstrate the efficiency of the presented algorithm. Comparison of obtained results clearly signifies the efficiency of the CBO method in reducing the WDSs construction cost and computational time of the analysis.

---

This paper presents an improved multi-objective group search optimizer (IMGSO) that is based on Pareto theory that is designed to handle multi-objective optimization problems. The optimizer includes improvements in three areas: the transition-feasible region is used to address constraints, the Dealer’s Principle is used to construct the non-dominated set, and the producer is updated using a tabu search and a crowded distance operator. Two objective optimization problems, the minimum weight and maximum fundamental frequency, of four truss structures were optimized using the IMGSO. The results show that IMGSO rapidly generates the non-dominated set and is able to handle constraints. The Pareto front of the solutions from IMGSO is clearly dominant and has good diversity.

---

This paper presents an efficient method for updating the structural finite element model. Model updating is performed through minimizing the difference of recorded acceleration of real damaged structure and hypothetical damaged structure, by updating physical parameters in each phase using iterative process of Levenberg-Marquardt algorithm. This algorithm is based on sensitivity analysis and provides a linear solution for nonlinear damage detection problem. The presented method is capable of detecting the exact location and ratio of structural damage in the presence of noise or incomplete data.

---

In the present study, two new hybrid approaches are proposed for predicting peak ground acceleration (PGA) parameter. The proposed approaches are based on the combinations of Adaptive Neuro-Fuzzy System (ANFIS) with Genetic Algorithm (GA), and with Particle Swarm Optimization (PSO). In these approaches, the PSO and GA algorithms are employed to enhance the accuracy of ANFIS model. To develop hybrid models, a comprehensive database from Pacific Earthquake Engineering Research Center (PEER) are used to train and test the proposed models. Earthquake magnitude, earthquake source to site distance, average shear-wave velocity, and faulting mechanisms are used as predictive parameters. The performances of developed hybrid models (PSO-ANFIS-PSO and GA-ANFIS-GA) are compared with the ANFIS model and also the most common soft computing approaches available in the literature. According to the obtained results, three developed models can be effectively used to predict the PGA parameter, but the comparison of models shows that the PSO-ANFIS–PSO model provides better results.

---

For seismic resistant design of critical structures, a dynamic analysis, based on either response spectrum or time history is frequently required. Due to the lack of recorded data and randomness of earthquake ground motion that might be experienced by the structure under probable future earthquakes, it is usually difficult to obtain recorded data which fit the necessary parameters (e.g. soil type, source mechanism, focal depth, etc.) well. In this paper, a new method for generating artificial earthquake accelerograms from the target earthquake spectrum is suggested based on the use of wavelet analysis and artificial neural networks. This procedure applies the learning capabilities of neural network to expand the knowledge of inverse mapping from the response spectrum to the earthquake accelerogram. At the first step, wavelet analysis is utilized to decompose earthquake accelerogram into several levels, which each of them covers a special range of frequencies. Then for every level, a neural network is trained to learn the relationship between the response spectrum and wavelet coefficients. Finally, the generated accelerogram using inverse discrete wavelet transform is obtained. In order to make earthquake signals compact in the proposed method, the multiplication sample of LPC (Linear predictor coefficients) is used. Some examples are presented to demonstrate the effectiveness of the proposed method.

---

The objective of this work is to predict the temperature of the different types of walls which are Ferro cement wall, reinforced cement concrete (RCC) wall and two types of cavity walls (combined RCC with Ferrocement and combined two Ferro cement walls) with the help of mathematical modeling. The property of low thermal transmission of small air gap between the constituents of combine materials has been utilized to obtain energy efficient wall section. Ferro cement is a highly versatile form of reinforced concrete made up of wire mesh, sand, water, and cement, which possesses unique qualities of strength and serviceability. The significant intention of the proposed technique is to frame a mathematical modeling with the aid of optimization techniques. Mathematical modeling is done by minimizing the cost and time consumed in the case of extension of the existing work. Mathematical modeling is utilized to predict the temperature of the different wall such as RCC wall, Ferro cement, combined RCC with Ferro cement and combined Ferro cement wall. The different optimization algorithms such as Social Spider Optimization (SSO), Genetic Algorithm (GA) and Group Search Optimization (GSO) are utilized to find the optimal weights α and β of the mathematical modeling. All optimum results demonstrate that the attained error values between the output of the experimental values and the predicted values are closely equal to zero with the SSO model. The results of the proposed work are compared with the existing methods and the minimum errors with SSO algorithm for the case of two combined RCC wall was found to be less than 2%.

---

Digging of geotechnical boreholes and soil resistance tests are time-consuming and expensive activities. Therefore selection of optimum number and suitable location of boreholes can reduce cost of their drilling and soil resistance tests. In this research, a model which is consisting of geo statistics model as an estimator and an optimized model is selected. The kriging calculates the variance of the estimation error of different combinations from available geotechnical boreholes. In each combination, n is number of considered boreholes and N is number of available boreholes (N>n). At the end, the best combination is selected by genetic algorithm (the error variance of this combination is minimum). Also the Kean Shahr of Ahvaz city (in Khuzestan province, Iran) is selected as case study in this research. Location of selected boreholes is in points that soil resistance of these points represents mean soil resistance of total region. Optimum number of boreholes is 15. Also results show that location of selected boreholes depends to soil resistance and diameter and length of applied piles are not important for this purpose.

---

An effective method utilizing the differential evolution algorithm (DEA) as an optimisation solver is suggested here to detect the location and extent of single and multiple damages in structural systems using time domain response method. Changes in acceleration response of structure are considered as a criterion for damage occurrence. The acceleration of structures is obtained using Newmark method. Damage is simulated by reducing the elasticity modulus of structural members. Three illustrative examples are numerically investigated, considering also measurement noise effect. All the numerical results indicate the high accuracy of the proposed method for determining the location and severity of damage.

---

In this study, the complex behavior of steel encased reinforced concrete (SRC) composite beam–columns in biaxial bending is predicted by multilayer perceptron neural network. For this purpose, the previously proposed nonlinear analysis model, mixed beam-column formulation, is verified with biaxial bending test results. Then a large set of benchmark frames is provided and P-M_x-M_y triaxial interaction curve is obtained for them. The specifications of these frames and their analytical results are defined as inputs and targets of artificial neural network and a relatively accurate estimation model of the nonlinear behavior of these beam-columns is presented. In the end, the results of neural network are compared to some analytical examples of biaxial bending to determine the accuracy of the model.

---

Vibrating particles system (VPS) is a new meta-heuristic algorithm based on the free vibration of freedom system’ single degree with viscous damping. In this algorithm, each agent gradually approach to its equilibrium position; new agents are generated according to current agents and a historically best position. Enhanced vibrating particles system (EVPS) employs a new alternative procedure to enhance the performance of the VPS algorithm. Two different truss structures are investigated to demonstrate the performance of the VPS and EVPS weight optimization of structures.

---

Concrete is the second most consumed material after water and the most widely used construction material in the world. The compressive strength of concrete is one of its most important mechanical properties, which highly depends on its mix design. The present study uses the intelligent methods with instance-based learning ability to predict the compressive strength of concrete. To achieve this objective, first, a set of data pertaining to concrete mix designs containing fly ash was collected. Then, mix design parameters were used as the inputs of the artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) developed for predicting the compressive strength. In all these models, prediction accuracy largely depends on the parameters of the learning model. Hence, the particle swarm optimization (PSO) algorithm, as a powerful population-based algorithm for solving continuous and discrete optimization problems, was used to determine the optimal values of algorithm parameters. The hybrid models were trained and tested with 426 experimental data and their results were compared by statistical criteria. Comparing the results of the developed models with the real values showed that the ANFIS-PSO hybrid model has the best performance and accuracy among the assessed methods.

---

The existence of recorded accelerograms to perform dynamic inelastic time history analysis is of the utmost importance especially in near-fault regions where directivity pulses impose extreme demands on structures and cause widespread damages. But due to the scarcity of recorded acceleration time histories, it is common to generate proper artificial ground motions. In this paper an alternative approach is proposed to generate near-fault pulse-like ground motions. A smoothening approach is taken to extract directivity pulses from an ensemble of near-fault pulse-like ground motions. First, it is proposed to simulate nonpulse-type ground motion using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Wavelet Packet Transform (WPT). Next, the pulse-like ground motion is produced by superimposing directivity pulse on the previously generated nonpulse-type motion. The main objective of this study is to generate near-field spectrum compatible records. Particle Swarm Optimization (PSO) is employed to optimize both the parameters of pulse model and cluster radius in subtractive clustering and Principle Component Analysis (PCA) is used to reduce the dimension of ANFIS input vectors. Artificial records are generated for the first, second and third level of wavelet packet decomposition. Finally, a number of interpretive examples are presented to show how the method works. The results show that the response spectra of generated records are decently compatible with the target near-field spectrum, which is the main objective of the study.