International Journal of Optimization in Civil Engineering
Volume:3 Issue: 4, Autumn 2013

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.

This paper, we presents a new primary-secondary-criteria scheduling model for resource-constrained project scheduling problem (RCPSP) with uncertain activity durations (UD) and cash flows (UC). The RCPSP-UD-UC approach producing a “robust” resource-feasible schedule immunized against uncertainties in the activity durations and which is on the sampling-based scenarios may be evaluated from a cost-oriented point of view. In the presented approach, it is assumed that each activity-duration and each cash flow value is an uncertain-but-bounded parameter, which is characterized by its optimistic and pessimistic estimations. The evaluation of a given robust schedule is based on the investigation of variability of the makespan as a primary and the net present value (NPV) as secondary criterion on the set of randomly generated scenarios given by a sampling-on-sampling-like process. Theoretically, the robust schedule-searching algorithm is formulated as a mixed integer linear programming problem, which is combined with a cost-oriented sampling-based approximation phase. In order to illustrate the essence of the proposed approach we present detailed computational results for a larger and very challenging project instance. A problem specific fast and efficient harmony search algorithm for large uncertain problems will be presented in a forthcoming paper.

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.

This paper attempts to improve the computational efficiency of the well known particle swarm optimization (PSO) algorithm for tackling discrete sizing optimization problems of steel frame structures. It is generally known that, in structural design optimization applications, PSO entails enormously time-consuming structural analyses to locate an optimum solution. Hence, in the present study it is attempted to lessen the computational effort of the algorithm, using the so called upper bound strategy (UBS), which is a recently proposed strategy for reducing the total number of structural analyses involved in the course of design optimization. In the UBS, the key issue is to identify those candidate solutions which have no chance to improve the search during the optimum design process. After identifying those non-improving solutions, they are directly excluded from the structural analysis stage, diminishing the total computational cost. The performance of the UBS integrated PSO algorithm (UPSO) is evaluated in discrete sizing optimization of a real scale steel frame to AISC-LRFD specifications. The numerical results demonstrate that the UPSO outperforms the original PSO algorithm in terms of the computational efficiency.

The objective of this paper is to present an optimal design for single-layer barrel vault frames via improved magnetic charged system search (IMCSS) and open application programming interface (OAPI). The IMCSS algorithm is utilized as the optimization algorithm and the OAPI is used as an interface tool between analysis software and the programming language. In the proposed algorithm, magnetic charged system search (MCSS) and improved harmony search (IHS) are utilized to achieve a good convergence and good solutions especially in final iterations. The results confirm the efficiency of OAPI as a powerful interface tool in the analysis process of barrel vault structures and also the ability of IMCSS algorithm in fast convergence and achieving optimal results.

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.

This article presents a comprehensive review of chaos embedded meta-heuristic optimization algorithms and describes the evolution of this algorithms along with some improvements, their combination with various methods as well as their applications. The reported results indicate that chaos embedded algorithms may handle engineering design problems efficiently in terms of precision and convergence and, in most cases; they outperform the results presented in the previous works. The main goal of this paper is to providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.

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.

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.

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.