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The Isogeometric Analysis (IA) is utilized for structural topology optimization  considering minimization of weight and local stress constraints. For this purpose, material density of the structure  is  assumed  as  a  continuous  function  throughout  the  design  domain  and approximated using the Non-Uniform Rational B-Spline (NURBS) basis functions. Control points of the density surface are considered as design variables of the optimization problem that can change the topology during the optimization process. For initial design, weight and stresses of the structure are obtained based on full material density over the design domain. The  Method  of  Moving  Asymptotes  (MMA)  is  employed  for  optimization  algorithm. Derivatives of the objective function and constraints with respect to the design variables are determined  through  a  direct  sensitivity  analysis.  In  order  to  avoid  singularity  a  relaxation technique  is  used  for  calculating  stress  constraints.  A  few  examples  are  presented  to demonstrate the performance of the method. It is shown that using the IA method and an appropriate stress relaxation technique can lead to reasonable optimum layouts.

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The paper deals with the reliability–based design optimization (RBDO) of concrete gravity dams subjected to earthquake load using subset simulation. The optimization problem is formulated such that the optimal shape of concrete gravity dam described by a number of variables is found by minimizing the total cost of concrete gravity dam for the given target reliability. In order to achieve this purpose, a framework is presented whereby subset simulation is integrated with a hybrid optimization method to solve the RBDO approach of concrete gravity dam. Subset simulation with Markov Chain Monte Carlo (MCMC) sampling is utilized to estimate accurately the failure probability of dams with a minimum number of samples. In this study, the concrete gravity dam is treated as a two–dimensional structure involving the material nonlinearity effects and dam–reservoir–foundation interaction. An efficient metamodel in conjunction with subset simulation–MCMC is provided to reduce the computational cost of dynamic analysis of dam–reservoir–foundation system. The results demonstrate that the RBDO approach is more appropriate than the deterministic optimum approach for the optimal shape design of concrete gravity dams.

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In the present study, two different data-driven models, artificial neural network (ANN) and multiple linear regression (MLR) models, have been developed to predict the 28 days compressive strength of concrete. Seven different parameters namely 3/4 mm sand, 3/8 mm sand, cement content, gravel, maximums size of aggregate, fineness modulus, and water-cement ratio were considered as input variables. For each set of these input variables, the 28 days compressive strength of concrete were determined. A total number of 140 input-target pairs were gathered, divided into 70%, 15%, and 15% for training, validation, and testing steps in artificial neural network model, respectively, and divided into 85% and 15% for training and testing steps in multiple linear regression model, respectively. Comparing the testing steps of both of the models, it can be concluded that the artificial neural network model is more capable in predicting the compressive strength of concrete in compare to multiple linear regression model. In other words, multiple linear regression model is better to be used for preliminary mix design of concrete, and artificial neural network model is recommended in the mix design optimization and in the case of higher accuracy requirements.

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This paper presents a novel population-based meta-heuristic algorithm inspired by the game of tug of war. Utilizing a sport metaphor the algorithm, denoted as Tug of War Optimization (TWO), considers each candidate solution as a team participating in a series of rope pulling competitions.  The  teams  exert  pulling  forces  on  each  other  based  on  the  quality  of  the solutions  they  represent.  The  competing  teams  move  to  their  new  positions  according  to Newtonian laws of mechanics. Unlike many other meta-heuristic methods, the algorithm is formulated  in  such  a  way  that  considers  the  qualities  of  both  of  the  interacting  solutions. TWO  is  applicable  to  global  optimization  of  discontinuous,  multimodal,  non-smooth,  and non-convex functions. Viability of the proposed method is examined using some benchmark mathematical functions and engineering design problems. The numerical results indicate the efficiency of the proposed algorithm compared to some other methods available in literature.

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In this paper, two different data driven models, genetic programming (GP) and multivariate adoptive regression splines (MARS), have been adopted to create the models for prediction of bridge risk score. Input parameters of bridge risks consists of safe risk rating (SRR), functional risk rating (FRR), sustainability risk rating (SUR), environmental risk rating (ERR) and target output. The total dataset contains 66 bridges data in which 70% of dataset is taken as training and the remaining 30% is considered for testing dataset. The accuracy of the models are determined from the coefficient of determination (R²). If the R² the testing model is close to the R² value of the training model, that particular model is to be consider as robust model. The modeling mechanisms and performance is quite different for both the methods hence comparative study is carried out. Thus concluded robust models performance based on the R² value, is checked with mathematical statistical equations.  In this study both models were performed, examined and compared the results with mathematical methods successfully. From this work, it is found that both the proposed methods have good capability in predestining the results. Finally, the results reveals that genetic Programming is marginally outperforms over the MARS technique.

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Beginning  in  2011  an  international  academic  contest  named  as  International  Student Competition  in  Structural  Optimization  (ISCSO)  has  been  organized  by  the  authors  to encourage undergraduate and graduate students to solve structural engineering optimization problems. During the past events on the one hand a unique platform is provided for a fair comparison of structural optimization algorithms; and on the other hand it is attempted to draw  the  attention  of  students  to  the  interesting  and  joyful  aspects  of  dealing  with optimization problems. This year, after five online events successfully held  with support and help of our advisory and scientific committee members from different universities all around the world, the authors  decided to gather the  test problems of the  ISCSO in this  technical report as an optimization test set. Beside the well -known traditional benchmark instances, the  provided  test  set  might  also  be  used  for  further  performance  evaluation  of  future structural optimization algorithms.

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This research deals with the development and comparison of two data-driven models, i.e., Artificial Neural Network (ANN) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) models for estimation of 28-day compressive strength of concrete for 160 different mix designs. These various mix designs are constructed based on seven different parameters, i.e., 3/4 mm sand, 3/8 mm sand, cement content, maximum size of aggregate, gravel content, water-cement ratio, and fineness modulus. In this study, it is found that the ANN model is an efficient model for prediction of compressive strength of concrete. In addition, ANFIS model is a suitable model for the same estimation purposes, however, the ANN model is recognized to be more fitting than ANFIS model in predicting the 28-day compressive strength of concrete.

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This paper combines particle swarm optimization, grid search method and univariate method as a general optimization approach for any type of problems emphasizing on optimum design of steel frame structures. The new algorithm is denoted as the GSU-PSO. This method attempts to decrease the search space and only searches the space near the optimum point. To achieve this aim, the whole search space is divided into a series of grids by applying the grid search method. By using a method derived from the univariate method, the variables of the best particle change values. Finally, by considering an interval adjustment to the variables and generating particles randomly in new intervals, the particle swarm optimization allows us to swiftly find the optimum solution. This method causes converge to the optimum solution more rapidly and with less number of analyses involved. The proposed GSU-PSO algorithm is tested on several steel frames from the literature. The algorithm is implemented by interfacing MATLAB mathematical software and SAP2000 structural analysis code. The results indicated that this method has a higher convergence speed towards the optimal solution compared to the conventional and some well-known meta-heuristic algorithms. In comparison to the PSO algorithm, the proposed method required around 45% of the total number of analyses recorded and improved marginally the accuracy of solutions.

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Critical Path Method (CPM) is one of the most popular techniques used by construction practitioners for construction project scheduling since the 1950s. Despite its popularity, CPM has a major shortcoming, as it is schedule based on two impractical acceptance that the project deadline is not bounded and that resources are unlimited. The analytical competency and computing capability of CPM thus need to be enhanced by applying some additional techniques like Time-Cost Trade-off (TCT) and Constraint Resource Scheduling (CRS) separately after the initial schedule is determined. Therefore, this paper is focusing on an effective method for considering simultaneously TCT and CRS using a nonlinear integer framework, taking help of Microsoft Project Software (MSP) and Microsoft Excel Solver. Through this method, first, a start delay technique is applied to the baseline schedule to level out the resource over allocation and then the project network diagram is modified according to the resource-leveled schedule. Secondly, a time-cost optimization is used over the resource-leveled schedule network diagram, using MS Excel solver to get the optimum duration associated with the minimum total cost of the project satisfying resource constraint. The proposed framework using overtime for activity expedition, and required less time to generate the final solution compare to the available methods considering TCT+CRS simultaneously.

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The first step in the design of plate girder is to estimate the self-weight of it. Although empirical formulae for the same are available, the level of their accuracy (underestimate or overestimate) with respect to actual self-weight is not known. In this paper, optimized sections are obtained for different spans subjected to different live load carrying capacities and self-weights are estimated. EXCEL solver, which adopts Reduced Gradient Method (RGM) was applied for optimization. The objective function was chosen as Cross-sectional area with twelve constraints based on LRFD (IS 800: 2007) design specification for safety and serviceability. Simply supported (laterally restrained) plastic symmetric cross section without stiffeners is adopted for study. A mathematical model was developed based on best-fit curves between self-weight, span and live load carrying capacity and their trend line equations are obtained. The study revealed that, the ratio of self-weight to load carrying capacity was parabolic for a given span. The results from this equation are compared with the conventional formula and the standard deviation of the proposed model with respect to actual self-weight is in the range of -0.03 to 2.29 while that from the conventional model is in the range of -0.04 to 9.18.

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Calculation of lateral earth pressure on retaining walls is one of the main issues in geotechnics. The upper and lower bound theorems of plasticity are used to analyze the stability of geotechnical structures include bearing capacity of foundations, lateral earth pressure on retaining walls and factor of safety of slopes. In this paper formulation of finite element limit analysis is introduced to determine plastic limit load in the perfect plastic materials. Elements with linear strain rates, which are used in the formulation, cause to eliminate the necessity of velocity discontinuities between the elements. Using non-linear programming based on second order cone programming (SOCP), which has good conformity with cone yield functions such as Mohr-Coulomb and Drucker-Prager, is another important advantage that remove the problem of using ordinary linear programming algorithms for yield functions such as divergent in the apexes. Finally, the optimization problem will be solved by mathematical method. The proposed method is used for calculating active earth pressure on retaining walls in cohesive-frictional soils. According to results of analysis, active earth force on retaining wall is decreased by increasing soil cohesion, wall inclination friction angle between backfill and wall and friction angle of soil wherein the force is increased by increasing surcharge on the backfill and the backfill slope. Mathematical method is used for obtaining accurate results in this research, however, heuristic methods are used when approximate solutions are sufficient.

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Offshore jacket-type towers are steel structures designed and constructed in marine environments for various purposes such as oil exploration and exploitation units, oceanographic research, and undersea testing. In this paper a newly developed meta-heuristic algorithm, namely Cyclical Parthenogenesis Algorithm (CPA), is utilized for sizing optimization of a jacket-type offshore structure. The algorithm is based on some key aspects of the lives of aphids as one of the highly successful organisms, especially their ability to reproduce with and without mating. The optimal design procedure aims to obtain a minimum weight jacket-type structure subjected to API-RP 2A-WSD specifications. SAP2000 and its Open Application Programming Interface (OAPI) feature are utilized to model the jacket-type structure and the corresponding loading. The results of the optimization process are then compared with those of Particle Swarm Optimization (PSO) and its democratic version (DPSO).

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Growing tendency for Urbanization and rapid development of the cities has resulted in urban neighborhoods obstructing the access of each other to the natural sources e.g. solar energy, natural ventilation. Sunlight as the main part of input energy in urban energy balance equation and natural lighting is of vital importance. This paper attempts to achieve an optimum morphology for residential blocks in urban area with the highest exposure to the sunlight. To reach this goal a pilot area in Tabriz’s downtown was selected and regarding solar angle, local street regulations and the width of surrounding streets 3 different scenarios for the buildings blocks were defined. Using a three-dimensional microclimate model, ENVI-met, solar access of defined scenarios was calculated for the longest and the shortest day of the year. Results showed that Type C2 (highest, more open spaces) is a more efficient style for winter times as it receives more of the sun’s energy and also the amount of sun it gets during a day and type B2 (medium open space and height) is the better for summer as it gets less energy from the sun and it is exposed to sunlight less than other types in a hot summer day.

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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.

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In the present study, the multivariate adaptive regression splines (MARS) technique is employed to estimate the drying shrinkage of concrete. To this purpose, a very big database (RILEM Data Bank) from different experimental studies is used. Several effective parameters such as the age of onset of shrinkage measurement, age at start of drying, the ratio of the volume of the sample on its drying surface, relative humidity, cement content, the ratio between water and cement contents, the ratio of sand on total aggregate, average compressive strength at 28 days, and modulus of elasticity at 28 days are included in the developing process of MARS model. The performance of MARS model is compared with several codes of practice including ACI, B3, CEB MC90-99, and GL2000. The results confirmed the superior capability of developed MARS model over existing design codes. Furthermore, the robustness of the developed model is also verified through sensitivity and parametric analyses.

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In this study, constant-ductility optimization algorithm under a family of earthquake ground motions is utilized to achieve uniform damage distribution over the height of steel moment resisting frames (SMRFs). SMRF structures with stiffness-degrading hysteric behavior are modeled as single-bay generic frame in which the plastic hinge is confined only at the beam ends and the bottom of the first story columns. Several SMRFs having different fundamental periods and number of stories are optimized such that a uniform story damage (ductility demand) is obtained under a given earthquake ground motion. Then, the optimum lateral load pattern derived from the optimization process is compared with that of the design load pattern proposed by the latest version of the Iranian code of practice, Standard No. 2800 to evaluate the adequacy of the seismic code design pattern. Results of this study indicate that, generally, the average story shear strength profiles corresponding to the optimum seismic design are significantly different from those of the Standard No. 2800 story shear strength pattern. In fact, the height-wise distribution of story ductility demands resulted from utilizing code-based design lateral load pattern are very non-uniform when compared to the corresponding optimum cases. In addition, a significant dependency is found between the average story shear strength pattern and inelastic behavior of structural elements.

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For application of the plastic analysis and design methods the control of the plastic behaviour of the structures is an important requirement. In this study, the complementary strain energy of the residual forces is considered as an overall measure of the plastic performance of the structure. Shakedown theorem for the analysis of the plastic behaviour of the laterally loaded piles is developed and applied to single vertical long pile. Limit curves are presented for the shakedown load multipliers. The formulations of the problems lead to mathematical programming which are solved by the use of nonlinear algorithm.

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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.

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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.

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In this article, by Partitioning of designing space, optimization speed is tried to be increased by GA. To this end, designing space search is done in two steps which are global search and local search. To achieve this goal, according to meshing in FEM, firstly, the list of sections is divided to specific subsets. Then, intermediate member of each subset, as representative of subset, is defined in a new list. Optimization process is started based on the new list of sections which includes subset’s representatives (global search). After some specific generations, range of optimum design is indicated for each designing variable. Afterwards, the list of sections is redefined relative to previous step’s result and based on subset of relevant variable. Finally, optimization will be continued based on the new list of sections for each designing variable to complete the generations (local search). In this regard, effect of dimension and number of subset’s members of global and local searches in proposal are investigated by optimization examples of skeletal structures. Results imply on optimization speed enhancement based on proposal in different cases proportional to simple and advanced cases of GA.