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In this paper, we present a new idea for robust project scheduling combined with a cost-oriented uncertainty investigation. The result of the new approach is a makespan minimal robust proactive schedule, which is immune against the uncertainties in the activity durations and which can be evaluated from a cost-oriented point of view on the set of the uncertain-but-bounded duration and cost parameters using a sampling-based approximation. In this paper, we assume that the sources of uncertainty are the variability of the activity durations and the cash flow values, and present an appropriate hybrid method, which is a combination of mathematical programming, metaheuristic and sampling-based elements, to cope with this "uncertainty in uncertainty" like real problem.

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Network design problem is one of the most complicated and yet challenging problems in transportation planning. The Bi-level, non-convex and integer nature of network design problem has made it into one of the most complicated optimization problems. Inclusion of time dimension to the classical network design problem could add to this complexity. In this paper an Ant Colony System (ACS) has been proposed to solve the Time Dependent Network Design Problem (T-NDP). The proposed algorithm has been used to solve different networks: A small size, a medium size and a large scale network. The results show that the proposed model has superior performance compared to the previous method proposed for solving the T-NDP.

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Highway construction projects are one of the most important construction projects in the world. Therefore predicting the time of these kinds of projects is important. Basically highway projects are including few activities which are repeating along the horizontal direction. One of the best methods for scheduling these types of projects is linear scheduling method. The repetitive nature of the highway activities is a good reason for schedulers to use linear scheduling methods in order to estimate the time of the project. One of the most important factors in linear projects is considering the effect of the activities productivity on scheduling. The first part of the research has been proposed to quantify the main equation of the identified factors for predicting the daily production rates of the embankment activity. The second part is scheduling the highway construction projects by developing the LSMvpr method based on the application of the embankment activity productivity equation. The purpose of the research is to develop the LSMvpr method for scheduling the highway construction projects by considering the concept of activity productivity in the shape of an equation varying by independent variables changes. By the use of multiple regression analysis the coefficients of affecting factors have been calculated in order to gain a production rate equation for predicting the embankment activity productivity. A software package has been presented for scheduling a highway construction project by coding in MATLAB. The offered software used for validating the model for scheduling the highway construction projects.

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Weighted Uniform Simulation (WUS) is recently presented as one of the efficient simulation methods to obtain structural failure probability and most probable point (MPP). This method requires initial assumptions of failure probability to obtain results. Besides, it has the problem of variation in results when it conducted with few samples. In the present study three strategies have been presented that efficiently enhanced capabilities of WUS. To this aim, a progressively expanding intervals strategy proposed to eliminate the requirement to initial assumptions in WUS, while low-discrepancy samples simultaneously employed to reduce variations in failure probabilities. Moreover, to improve the accuracy of MPP, a new simple local search method proposed and combined with the simulation that strengthened the method to obtain more accurate MPP. The capabilities of proposed strategies investigated by solving several structural reliability problems and obtained results compared with traditional WUS and common reliability methods. Results show that proposed strategies efficiently improved the capabilities of conventional WUS.

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In this paper, a two-stage method for damage detection and estimation in tall shear frames is presented. This method is based on the first mode shape of a shear frame. We demonstrate that the first mode shape slope is very sensitive to the story stiffness. Thus, at the first stage, by using the grey system theory on the first mode shape slope, damage locations are identified in shear frames. Damage severity is determined at the second stage by defining the damage detection problem as an optimization problem by using grey relation coefficients. The optimization problem is solved by a socio-politically motivated global search strategy which is the imperialist competitive algorithm. The efficiency and robustness of the proposed method for the identification and estimation of damages in tall shear frames were studied by using two numerical examples. In addition, the capability of the presented method in real conditions was demonstrated by contaminating of modal data with different levels of random noises. All the obtained results from the numerical studies are shown the good performance of the presented method in the damage localization and quantification of tall buildings.

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The development of an excavation damaged zone (EDZ) around an underground excavation can change the physical, mechanical and hydraulic behaviors of the rock mass near an underground space. This might result in endangering safety, achievement of costs and excavation planed. This paper presents an approach to build a prediction model for the assessment of EDZ, based upon rock mass characteristics changed. Rock engineering systems (RES) was used as an appropriate method for choosing the best parameter that expresses the occurrence of EDZ. Modulus of deformation with the highest weight in the system was selected as the most effective changed parameter. The adaptive network-based fuzzy inference system (ANFIS) with modulus of deformation as input was used to build a prediction model for the assessment of EDZ. Three ANFIS models were implemented, grid partitioning (GP), subtractive clustering method (SCM) and fuzzy c-means clustering method (FCM). A comparison was made between these three models and the results show the superiority of the ANFIS-SCM model. Furthermore, a case study in a test gallery of the Gotvand dam, Iran was carried out to illustrate the capability of the ANFIS model defined.

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In the present study, the reliability assessment of performance-based optimally seismic designed reinforced concrete (RC) and steel moment frames is investigated. In order to achieve this task, an efficient methodology is proposed by integrating Monte Carlo simulation (MCS) and neural networks (NN). Two NN models including radial basis function (RBF) and back propagation (BP) models are examined in this study. In the proposed methodology, MCS is used to estimate the total exceedence probability associated with immediate occupancy (IO), life safety (LS) and collapse prevention (CP) performance levels. To reduce the computational burden of MCS process, the required nonlinear responses of the generated structures are predicted by RBF and BP models. The numerical results imply the superiority of BP to RBF in prediction of structural responses associated with performance levels. Finally, the obtained results demonstrate the high efficiency of the proposed methodology for reliability assessment of RC and steel frame structures.

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A quick group search optimizer (QGSO) is an intelligent optimization algorithm which has been applied in structural optimal design, including the hinged spatial structural system. The accuracy and convergence rate of QGSO are feasible to deal with a spatial structural system. In this paper, the QGSO algorithm optimization is adopted in seismic research of steel frames with semi-rigid connections which more accurately reflect the practical situation. The QGSO is combined with the constraint from the penalty coefficients and dynamic time-history analysis. The performance of the QGSO on seismic design has been tested on a two-bay five-layer steel frame in this paper. The result shows that, compared with the PSO algorithm, the QGSO algorithm has better performance in terms of convergence rate and the ability to escape from local optimums. Moreover, it is feasible and effective to apply the QGSO to the seismic optimal design of steel framework.

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Computational cost of metaheuristic based optimum design algorithms grows excessively with structure size. This results in computational inefficiency of modern metaheuristic algorithms in tackling optimum design problems of large scale structural systems. This paper attempts to provide a computationally efficient optimization tool for optimum design of large scale steel frame structures to AISC-LRFD specifications. To this end an upper bound strategy (UBS), which is a recently proposed strategy for reducing the total number of structural analyses in metaheuristic optimization algorithms, is used in conjunction with an exponential variant of the well-known big bang-big crunch optimization algorithm. The performance of the UBS integrated algorithm is investigated in the optimum design of two large-scale steel frame structures with 3860 and 11540 structural members. The obtained numerical results clearly reveal the usefulness of the employed technique in practical optimum design of large-scale structural systems even using regular computers.

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Building performance simulation is being increasingly deployed beyond the building design phase to support efficient building operation. Specifically, the predictive feature of the simulation-assisted building systems control strategy provides distinct advantages in view of building systems with high latency and inertia. Such advantages can be exploited only if model predictions can be relied upon. Hence, it is important to calibrate simulation models based on monitored data. In the present paper, we report on the use of optimization-aided model calibration in the context of an existing university building. Thereby, our main objective is to deploy data obtained via the monitoring system to both populate the initial simulation model and to maintain its fidelity through an ongoing optimization-based calibration process. The results suggest that the calibration can significantly improve the predictive performance of the thermal simulation model.

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This paper presents an efficient meta-heuristic algorithm for optimization of double-layer scallop domes subjected to earthquake loading. The optimization is performed by a combination of harmony search (HS) and firefly algorithm (FA). This new algorithm is called harmony search firefly algorithm (HSFA). The optimization task is achieved by taking into account geometrical and material nonlinearities. Operation of HSFA includes three phases. In the first phase, a preliminary optimization is accomplished using HS. In the second phase, an optimal initial population is produced using the first phase results. In the last phase, FA is employed to find optimum design using the produced optimal initial population. The optimum design obtained by HSFA is compared with those of HS and FA. It is demonstrated that the HSFA converges to better solution compared to the other algorithms.

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Tunnel structures are known as expensive infrastructures and determining optimum designs of these structures can play a great role in minimizing their cost. The formulation of optimum design of industrial tunnel sections as an optimization is considered in this paper and then the enhanced charged system search, as a recently developed meta-heuristic approach, has been applied to solve the problem. The results and comparisons based on numerical examples show the efficiency of the optimization algorithm.

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Dynamic structural responses via time history analysis are highly dependent to characteristics of selected records as the seismic excitation. Ground motion scaling is a well-known solution to reduce such a dependency and increase reliability to the dynamic results. The present work, formulate a twofold problem for optimal spectral matching and performing consequent sizing optimization based on such scaled ground motion via numerical step-by-step analyses. Particle swarm optimization as a widely used meta-heuristic is specialized and improved to solve this problem treating a number of examples. The scaling error is evaluated using both traditional procedure and the developed method. In this regard, some issues are studied including the effect of structural period and shape of the design spectrum on the results. Contribution of the proposed enhancement on the standard particle swarm intelligence has improved its explorative capability resulting in higher efficiency of the algorithm.

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Colliding bodies optimization (CBO) is a new population-based stochastic optimization algorithm based on the governing laws of one dimensional collision between two bodies from the physics. Each agent is modeled as a body with a specified mass and velocity. A collision occurs between pairs of objects to find the global or near-global solutions. Enhanced colliding bodies optimization (ECBO) uses memory to save some best solutions and utilizes a mechanism to escape from local optima. The performances of the CBO and ECBO are shown through truss and frame design optimization problems. The codes of these methods are presented in MATLAB and C++.

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This article aims to study the self-supporting truss towers used to support large wind turbines. The goal is to evaluate and validate numerically by finite element method the structural analysis when the lattice structures of the towers of wind turbines are subjected to static loads and these from common usage. With this, it is expected to minimize the cost of transportation and installation of the tower and maximize the generation of electricity, considering technical standards and restrictions of structural integrity and safety, making vibration analysis and the required static and dynamic loads, thereby preventing failures by fractures or mechanical fatigue. Practical examples of towers will be designed by the system and will be tested in structural simulation programs using the Finite Element Method. This analysis is performed on the entire region coupling action of the turbine, with variable sensitivity to vibration levels. The results obtained for freestanding lattice tower are compared with the information of a tubular one designed to support the generator with the same characteristics. At the end of this work it was possible to observe the feasibility of using lattice towers that proved better as its structural performance but with caveats about its dynamic performance since the appearance of several other modes natural frequency thus reducing the intervals between them in low frequency and theoretically increase the risk of resonance.

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In the area of semi-active control of civil structures, Magneto-Rheological (MR) damper has been an efficient mechanism for reducing the seismic response of structures. In this paper, an effective method based on defining an optimization problem for designing MR dampers has been proposed. In the proposed method, the parameters of semi-active control system are determined so that the maximum response of structure is minimized. To solve the optimization problem, the Genetic algorithm (GA) has been utilized. The modified Bouc-Wen model has been used to represent the dynamic behavior of MR damper while to determine the input voltage at any time step, the clipped optimal control algorithm with LQR controller has been applied. To evaluate the performance of the proposed method, a ten-storey shear frame subjected to the El-Centro excitation and for two different kinds of objective functions, optimal MR dampers have been designed. Then the performance of optimal MR damper has been tested under different excitations. The results of the numerical simulations have shown the effectiveness of the proposed method in designing optimal MR dampers that have the capability of reducing the response of the structures up to a significant level. In addition, the effect of selecting a proper objective function to achieve the best performance of MR dampers in decreasing different responses of structure has been shown.

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Simplified Dolphin Echolocation (SDE) optimization is an improved version of the Dolphin Echolocation optimization. The dolphin echolocation (DE) is a recently proposed metaheuristic algorithm, which was imitated dolphin’s hunting process. The global or near global optimum solution modeled as dolphin’s bait, dolphins send sound in different directions to discover the best bait among their search space. This paper introduced a new optimization method called SDE for weight optimization of steel truss structures problems. SDE applies some new approaches for generating new solutions. These improvements enhance the accuracy and convergence rate of the DE SDE does not depend on any empirical parameter. The results of the SDE for mathematical and engineering optimization problems are compared to those of the standard DE and some popular metaheuristic algorithms. The results show that SDE is competitive with other algorithms.

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With the development of the technology and increase of human dependency on structures, healthy structures play an important role in people lives and communications. Hence, structural health monitoring has been attracted strongly in recent decades. Improvement of measuring instruments made signal processing as a powerful tool in structural heath monitoring. Wavelet transform invention causes a great evolution in signal processing. Wavelet transform decomposes a signal into several groups based on scaled and translated basic functions. In this study, a novel methodology based on wavelet transform using complex Morlet wavelet has been introduced for system identification. This process includes a multivariable constrained optimization problem for selecting suitable complex Morlet wavelet. Using selected wavelet, modal parameters and flexibility matrix of structure can be estimated properly. Because of small modal participation of higher mode using finite number of modes leads to flexibility matrix with acceptable accuracy. Since damages cause change in structural properties, a damage index based on flexibility matrix has been applied and its performance has been investigated in some structures.

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The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvements are implemented on the basic algorithms. The proposed MOOAs are examined for three truss optimization problems, and the results are compared to those of some other well-known methods. The numerical results demonstrate that the proposed MOOAs possess better computational performance compared to the other algorithms.

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