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An efficient selection operator for use in genetic search of pipe networks optimal design is introduced in this paper. The proposed selection scheme is the superior member of a family of improved selection operators developed in an attempt to more closely simulate the main features of the natural mating process which is not reflected in existing selection schemes. The mating process occurring in the nature exhibits two distinct features. First, there is a competition between phenotypes looking for the fittest possible mate which usually ends up with choosing a mate with more or less the same fitness. Second, and more importantly, the search for a mate is often confined to a community of phenotypes rather than the whole population. Four different selection operators simulating these features in a random and pre-determined manner are developed and tested. All the selection schemes exhibit good convergence characteristics, in particular the one in which both the size of the sub-community and the pair of the mates in the sub-community are determined randomly. The efficiency of the proposed selection operator is shown by applying the method for the optimal design of three well-known benchmark networks, namely two-loop, Hanoi and New-York networks. The proposed scheme produces results comparable to the best results presented in the literature with much less computational effort

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Genetic Algorithm is known as a generalized method of stochastic search and has been successfully applied to various types of optimization problems. By GA s it is expected to improve the solution at the expense of additional computational effort. One of the key points which controls the accuracy and convergence rate of such a process is the selected method of coding/decoding of the original problem variables and the discrete feasibility space to be searched by GAS. In this paper, a direct index coding (DIC) is developed and utilized for the discrete sizing optimization of structures. The GA operators are specialized and adopted for this kind of encoded chromosomes and are compared to those of traditional GA S. The well-known lO-bar truss example from literature is treated here as a comparison benchmark, and the outstanding computational efficiency and stability of the proposed method is illustrated. The application of the proposed encoding method is not limited to truss structures and can also be directly applied to frame sizing problems.

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Optimization is an important methodology for activities in planning and design. The transportation designers are able to introduce better projects when they can save time and cost of travel for project by optimization methods. Most of the optimization problems in engineering are more complicated than they can be solved by custom optimization methods. The most common and available methods are heuristic methods. In these methods, the answer will be close to the optimum answer but it isn’t the exact one. For achieving more accuracy, more time has been spent. In fact, the accuracy of response will vary based on the time spent. In this research, using the generic algorithms, one of the most effective heuristic algorithms, a method of optimization for urban streets direction will be introduced. Therefore model of decision making in considered one way – two way streets is developed. The efficiency of model in Qazvin network is shown and the results compared whit the current situation as case study. The objective function of the research is to minimize the total travel time for all users, which is one of the most used in urban networks objectives.

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A novel approach to determine optimal sampling locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimisation problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of sampling devices as a surrogate of sampling design cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal sampling locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic sampling design optimisation problem is solved for a number of randomly generated calibration model parameter samples.The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.

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This paper presents a hybrid approach to developing a short-term traffic flow prediction model. In this

approach a primary model is synthesized based on Neural Networks and then the model structure is optimized through

Genetic Algorithm. The proposed approach is applied to a rural highway, Ghazvin-Rasht Road in Iran. The obtained

results are acceptable and indicate that the proposed approach can improve model accuracy while reducing model

structure complexity. Minimum achieved prediction r2 is 0.73 and number of connection links at least reduced 20%

as a result of optimization.

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Although there is enough knowledge indicating on the influence of frequency content of input motion on the deformation demand of structures, state-of-the-practice seismic studies use the intensity measures such as peak ground acceleration (PGA) which are not frequency dependent. The v max/a max ratio of strong ground motions can be used in seismic hazard studies as the representative of frequency content of the motions. This ratio can be indirectly estimated by the attenuation models of PGA and PGV which are functions of earthquake magnitude, source to site distance, faulting mechanism, and local site conditions. This paper presents new predictive equations for v max/a max ratio based on genetic programming (GP) approach. The predictive equations are established using a reliable database released by Pacific Earthquake Engineering Research Center (PEER) for three types of faulting mechanisms including strikeslip, normal and reverse. The proposed models provide reasonable accuracy to estimate the frequency content of site ground motions in practical projects. The results of parametric study demonstrate that v max/a max increases through increasing earthquake moment magnitude and source to site distance while it decreases with increasing the average shear-wave velocity over the top 30m of the site.

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The congestion pricing has been discussed as a practical tool for traffic management on urban transport networks. The traffic congestion is defined as an external diseconomy on the network in transport economics. It has been proposed that the congestion pricing would be used to reduce the traffic on the network. This paper investigates the cordon-based second-best congestion-pricing problems on road networks, including optimal selection of both toll levels and toll locations. A road network is viewed as a directed graph and the cutest concept in graph theory is used to describe the mathematical properties of a toll cordon by examining the incidence matrix of the network. Maximization of social welfare is sought subject to the elastic-demand traffic equilibrium constraint. A mathematical programming model with mixed (integer and continuous) variables is formulated and solved by use of two genetic algorithms for simultaneous determination of the toll levels and cordon location on the networks. The model and algorithm are demonstrated in the road network of Mashhad CBD.

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In this paper, the optimal location and characteristics of TADAS dampers in moment resisting steel structures, considering the application of minimum number of TADAS dampers in a building as an objective function and the restriction for destruction of main members is studied. Genetic algorithm in first generation randomly produces different chromosomes representing unique TADAS dampers distributions in structure and the structure corresponding to each chromosome is time history analyzed. Then the damage index for each member and the average weighted damage index for all members are determined. Genetic algorithm evaluates the fitness of each chromosome then selection and crossover as logical operators and mutation as random operator effect the current generation's chromosomes according to their fitness and new chromosomes are generated. Accordingly, successive generations are reproduced in the same way until the convergence condition is fulfilled in final generation and four distributions are suggested as better options. Since these proposed distributions are selected under the one earthquake, therefore, it is better that the four new structures are cost-benefit analyzed in different earthquakes. Finally, the optimal placement for dampers is compared and selected based on a benefit to cost ratio, drift stories and the number of different TADAS types of such structures. The increase in amount of energy dissipated via dampers located in different floors as well as the status of plastic hinges in main members of the structure strengthened with optimum option are the proof of the optimal placement and suitable characteristics for dampers.

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In this paper, a new approach was presented for bus network design which took the effects of three out of four stages of the bus planning process into account. The presented model consisted of three majors steps 1- Network Design Procedure (NDP), 2- Frequency Determination and Assignment Procedure (FDAP), and 3- Network Evaluation Procedure (NEP). Genetic Algorithm (GA) was utilized to solve this problem since it was capable of solving large and complex problems. Optimization of bus assignment at depots is another important issue in bus system planning process which was considered in the presented model. In fact, the present model was tested on Mandl’s bus network which was a benchmark in Swiss network and was initially employed by Mandl and later by Baaj, Mahmassani, Kidwai, Chakroborty and Zhao. Several comparisons indicated that the model presented in this paper was superior to the previous models. Meanwhile, none of the previous approaches optimized depots assignment. Afterwards, sensitivity analysis on GA parameters was done and calculation times were presented. Subsequently the proposed model was evaluated thus, Mashhad bus network was designed using the methodology of the presented model.

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Due to its critical impact and significant destructive nature during and after seismic events, soil liquefaction and liquefactioninduced

lateral ground spreading have been increasingly important topics in the geotechnical earthquake engineering field

during the past four decades. The aim of this research is to develop an empirical model for the assessment of liquefaction-induced

lateral ground spreading. This study includes three main stages: compilation of liquefaction-induced lateral ground spreading

data from available earthquake case histories (the total number of 525 data points), detecting importance level of seismological,

topographical and geotechnical parameters for the resulted deformations, and proposing an empirical relation to predict

horizontal ground displacement in both ground slope and free face conditions. The statistical parameters and parametric study

presented for this model indicate the superiority of the current relation over the already introduced relations and its applicability

for engineers.

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Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial regression (EPR), classical genetic programming (GP), and gene expression programming (GEP), are utilized to obtain more accurate predictive settlement models. The models are developed using a large databank of standard penetration test (SPT)-based case histories. The values obtained from the new models are compared with those of the most precise models that have been previously proposed by researchers. The results show that the new EPR and GP-based models are able to predict the foundation settlement on cohesionless soils under the described conditions with R2 values higher than 87%. The artificial neural networks (ANNs) and genetic programming (GP)-based models obtained from the literature, have R2 values of about 85% and 83%, respectively which are higher than 80% for the GEP-based model. A subsequent comprehensive parametric study is further carried out to evaluate the sensitivity of the foundation settlement to the effective input parameters. The comparison results prove that the new EPR and GP-based models are the most accurate models. In this study, the feasibility of the EPR, GP and GEP approaches in finding solutions for highly nonlinear problems such as settlement of shallow foundations on granular soils is also clearly illustrated. The developed models are quite simple and straightforward and can be used reliably for routine design practice.

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In different projects the speed of different machinery can be estimated using manufacturer's handbooks and a number of modification factors to consider the environmental effects, type of the project and status of site management. Since the statuses of different factors of the domestic projects are totally different from those of the international projects, there is a wide discrepancy between the determined speed by handbooks and the actual values in the domestic projects. This paper is aimed to develop a fuzzy system to estimate soil excavation rates at earthmoving jobsites. The proposed fuzzy system is based on IF-THEN rules a genetic algorithm improves the overall accuracy. The obtained results clearly revealed the capability and applicability of the proposed system to properly estimate soil excavation speed. The average error of fuzzy system, handbook method and nearest neighbor interpolation are 10 , 92 and 32 percent, respectively.

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We consider the problem of continuous dynamic berth allocation to containerships in a tidal seaport. In some container ports, low water depth in coastal area causes many restrictions on providing vessel's services. Therefore, berth allocation planning for relatively large vessels with high draft is subject to tidal conditions when the vessels are in the access channel as from anchorage area to the quay. Tidal conditions sometimes have a significant effect on possibility of entrance and departure of these ships to or from ports. Shahid Rajaee Port Complex, Iran's largest container seaport and the case study of this research, located at northern coast of Persian Gulf and has low water depth in its area. Historical data of seaside operations in this port is applied to the proposed model. This model also takes into account the variations of water depth in different berths. Simultaneous programming for two or more container terminals and exertion of priority and precedency coefficients based on vessel size and voyage type altogether are other attributes of this model. Here, genetic algorithm in combination with pattern search algorithm was used for solving the problem. Computational experiments have indicated that the proposed heuristic is relatively effective just for small size instances.

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Engineering design usually requires considering multiple variances in a design and integrating them appropriately in order to achieve the most desirable alternative. This consideration takes particular importance in the constructional projects of civil engineering. However, frequently, the structural designer’s considerations in civil engineering teams contrast the stylish and creative novelties of architectures. Then, we should take up new methodologies to yield appropriate alternatives which meet artistic aspects of design and simultaneously satisfy the structural designer’s demands. Consequently, the process of design should incorporate the multi-fold aspects of engineer’s requirements and their personal preference. So, in this study, we preset a systematic approach, so-called desirability based design, to perform a directed multi-objective optimal design considering various aspects of a design, based on soft-computing methods. Fuzzy logic integrated with genetic algorithm is employed to build a decision-making fuzzy system based on expert knowledge. It will be employed to conduct the designing process. Illustrative examples show practicality and efficiency of the presented methodology in structural design of several space structures.

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From practical point of view, optimum design of structures under time variable loadings faces many challenges. Issues such as time-dependent behavior of constraints and the computational costs of the gradients could be mentioned. In order to prevent such difficulties, in this paper, response spectrum method has been utilized instead of applying direct time history method. Additionally, seismic design of structures is defined as a design for a specific response spectra not for an individual acceleration time history. Furthermore, here, in order to guarantee the global optimal designs, the obtained results from gradient-based method are compared with those from the discrete optimization technique (Genetic algorithm). As well, the P-Delta effects are considered in a seismic analysis. In addition, many practical constraints according to the Iranian national building code (NBC) are included in the optimization problem. The developed MATLAB based computer program is utilized to solve the numerical examples of low, intermediate and relatively high-rise braced and un-braced steel frames.