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In this paper, an efficient optimization algorithm is proposed based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to optimize truss structures. The proposed algorithm utilizes the PSO for finding high fitness regions in the search space and the SA is used to perform further investigation in these regions. This strategy helps to use of information obtained by swarm in an optimal manner and to direct the agents toward the best regions, resulting in possible reduction of the number of particles. To show the computational advantages of the new PSO-SA method, some benchmark numerical examples are studied. The PSO-SA algorithm converges to better or at least the same solutions, while the number of structural analyses is significantly reduced

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To ensure efficient performance of irrigation canals, the losses from the canals need to be minimized. In this paper a modified formulation is presented to solve the optimization model for the design of different canal geometries for minimum seepage loss, in meta-heuristic environment. The complex non-linear and non-convex optimization model for canal design is solved using a probabilistic search algorithm namely Probabilistic Global Search Lausanne (PGSL). The solutions are found to be competitive to those reported in literature while applied for different example problems. To suit for real field applications, three site specific constraints are considered and the sensitivity of solutions for the most popular trapezoidal canals is investigated. The study shows the potential of the proposed approach to perform optimal design of irrigation canals for minimum seepage loss.

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Optimum control of upstream pumping station in open channels with given constraint in downstream end is presented in this paper. The upstream control is capable of minimizing water level fluctuations in the channel in which the downstream pumping station causes an undesirable wave. The proposed method combines an unsteady non-uniform flow solver with shock-capturing capability, Fourier series and metaheuristic firefly algorithm. Fourier series is used to estimate the optimum inflow control and firefly algorithm is utilized to determine the unknown coefficients in the series. With a suitable objective function, the procedure generates the optimum inflow hydrograph that can effectively cancel destructive downstream waves. The results have been compared with the results obtained by a variational approach and show satisfactory improvement both in simplicity and the value of objective function.

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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.

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The overall cost of companies dealing with the distribution tasks is considerably affected by the way that distributing vehicles are procured. In this paper, a more practical version of capacitated vehicle routing problem (CVRP) in which the decision of purchase or hire of vehicles is simultaneously considered is investigated. In CVRP model capacitated vehicles start from a single depot simultaneously and deliver the demanded items of several costumers with known demands where each costumer must be met once. Since the optimal vehicle procurement cost is a function of total distance it traverses during the planning horizon, the model is modified in a way that the decision of purchasing or hiring of each vehicle is made simultaneously. The problem is formulated as a mixed integer programming (MIP) model in which the sum of net present value (NPV) of procurement and traveling costs is minimized. To solve the problem, a hybrid electromagnetism and parallel simulated annealing (PSA-EM) algorithm and a Shuffled Frog Leaping Algorithm (SFLA) are presented. Finally, the presented methods are compared experimentally. Although in some cases the SFLA algorithm yields better solutions, experimental results show the competitiveness of PSA-EM algorithm from the computational time and performance points of view.

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Earthquakes are one of the most destructive natural phenomena which consist of rapid vibrations of rock near the earth’s surface. Because of their unpredictable occurrence and enormous capacity of destruction, they have brought fear to mankind since ancient times. Usually the earthquake acceleration is noted from the equipment in crisp or exact form. But in actual practice those data may not be obtained exactly at each time step, rather those may be with error. So those records at each time step are assumed here as intervals. Then using those interval acceleration data, the structural responses are found. The primary background for the present study is to model Interval Artificial Neural Network (IANN) and to compute structural response of a structural system by training the model for Indian earthquakes at Chamoli and Uttarkashi using interval ground motion data. The neural network is first trained here for real interval earthquake data. The trained IANN architecture is then used to simulate earthquakes by feeding various intensities and it is found that the predicted responses given by IANN model are good for practical purposes. The above may give an idea about the safety of the structural system in case of future earthquakes. Present paper demonstrates the procedure for simple case of a simple shear structure but the procedure may easily be generalized for higher storey structures as well.

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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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Tower cranes are major and expensive equipment that are extensively used at building construction projects and harbors for lifting heavy objects to demand points. The tower crane locating problem to position a tower crane and supply points in a building construction site for supplying all requests in minimum time, has been raised from more than twenty years ago. This problem has already been solved by linear programming, but meta-heuristic methods spend less time to solving the problem. Hence, in this paper three newly developed meta-heuristic algorithms called CBO, ECBO, and VPS have been used to solve the tower crane locating problem. Three scenarios are studied to show the applicability and performance of these meta-heuristics.

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This study focuses on using an artificial neural network (ANN) based model for predicting the performance of high strength concrete (HSC) beams strengthened with surface mounted FRP laminates. Eight input parameters such as geometrical properties of the beam and mechanical properties of FRP laminates were considered for this study. Back propagation network with Lavenberg-Marquardt algorithm has been chosen for the proposed network, which has been implemented using the programming package MATLAB. In the present study, comparison has been made between the experimental results and those predicted through neural network modeling. The amount of MAPE and RMSE were predicted and were found to be acceptable range. The statistical indicators such as correlation co-efficient (r) and co-efficient of determination (R²) were also predicted to estimate the accuracy of results obtained through ANN modeling. The results predicted through ANN modeling exhibit good correlation with the experimental results.

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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.

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During the past two decades, some industries have been moving towards project-centered systems in many modern countries. Therefore, managing simultaneous projects with considering the limitations in resources, equipment and manpower is very crucial. In the real world, project-based organizations are always facing with two main important features. First, the construction projects are decentralized and their distances are long, and second, there are several construction projects undertaken at different time periods. Therefore, appropriate selection of projects with regard to the capabilities of the organization may lead with increasing an expected profitability. This paper investigates the multi-period decentralized multi construction-project and scheduling problem subject to resource constraints, optimal resource pool location, deterioration and batch ordering of nonrenewable resources altogether, for the first time in the literature. In order to describe the problem under consideration in this paper and obtaining the optimal solutions, a mixed integer linear programming model is developed. Finally, the impact of decision integration on the profit profile of an organization is comprehensively investigated by solving numerical examples and through developing some heuristic methods.

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The flow in sewers is a complete three phase flow (air, water and sediment). The mechanism of sediment transport in sewers is very important. In other words, the passing flow must able to wash deposited sediments and the design should be done in an economic and optimized way. In this study, the sediment transport process in sewers is simulated using a hybrid model. In other words, using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Particle Swarm Optimization (PSO) algorithm a hybrid algorithm (ANFIS-PSO) is developed for predicting the Froude number of three phase flows. This inference system is a set of if-then rules which is able to approximate non-linear functions. In this model, PSO is employed for increasing the ANFIS efficiency by adjusting membership functions as well as minimizing error values. In fact, the PSO algorithm is considered as an evolutionary computational method for optimizing the process continues and discontinues decision making functions. Additionally, PSO is considered as a population-based search method where each potential solution, known as a swarm, represents a particle of a population. In this approach, the particle position is changed continuously in a multidimensional search space, until reaching the optimal response and or computational limitations. At first, 127 ANFIS-PSO models are defined using parameters affecting the Froude number. Then, by analyzing the ANFIS-PSO model results, the superior model is presented. For the superior model, the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and the determination coefficient (R²) were calculated equal to 5.929, 0.324 and 0.975, respectively.

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Production planning and inventory control efforts are known as the driving engines of manufacturing systems. The manufacturers, competing to survive in these days’ competitive business environment, aim to satisfy customers’ needs. This requires a precise production plan throughout the supply chain. These days, because of the increasing costs of production and distribution, especially in the cement industry, and given the importance of this industry, investors seek to reduce the production costs as much as possible, to achieve a competitive advantage. In cement industry, main focuses are converging on the alternative fuels developments, optimization of furnace fuel consumptions and sustainable and green production considerations. In this study, a mathematical model is developed to investigate the cement production plan. The objective function is to minimize the total costs of a real case of cement industry. The proposed model is applied on a case of real world application at the West Azerbaijan’s Urmia Cement Company. Sensitivity analyses are carried out on the findings of the model. The proposed model has proven to be cost efficient.

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Budapest Közút is developing ROad Data Information System based on mobile laser scanning since 2013. All public roads (cca. 5000 km) are surveyed by MLS (Riegl VMX450) in survey grade accuracy and all visible road assets has been digitized and loaded to a complex 3D GIS environment. Since the first full coverage had been done in 2014 the whole city has also been updated - being one of the few large infrastructure in the World that has not one but multiple high accuracy 3D data for the whole network. The high level accuracy, the full coverage and the already available data updates allows Budapest to use the 3D data for multiple operational applications - from traffic- and road design to planning, from assets management to traffic safety analyst and municipality activities.
One of the most cutting-edge applications is the road surface analyst over time that allows the road management company to analyze and optimize different construction methods and is changes over the years.
One example for road quality analyst is the application of data support for PMS (Pavement Management System) how keeps this new component the road surface quality well?

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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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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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Shape optimization of a double-curved dam is formulated using control points for interpolation functions. Every design vector is decoded into the integrated water-dam-foundation rock model. An enhanced algorithm is proposed by hybridizing particle swarm algorithm with ant colony optimization and simulated annealing. The best experiences of the search agents are indirectly shared via pheromone trail deposited on a bi-partite characteristic graph. Such a stochastic search is further tuned by Boltzmann functions in simulated annealing. The proposed method earned the first rank in comparison with six well-known meta‑heuristic algorithms in solving benchmark test functions. It captured the optimal shape design of Morrow Point dam, as a widely addressed case-study, by 21% reduced concrete volume with respect to the common USBR design practice and 16% better than the particle swarm optimizer. Such an optimal design was also superior to the others in stress redistribution for better performance of the dam system.
 

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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.