International Journal of Optimization in Civil Engineering
Volume:6 Issue: 2, Spring 2016

The tunnel boring machine (TBM) penetration rate estimation is one of the crucial and complex tasks encountered frequently to excavate the mechanical tunnels. Estimating the machine penetration rate may reduce the risks related to high capital costs typical for excavation operation. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, establishing relationship between rock properties and TBM penetration rate is not a simple task and cannot be done using a simple linear or nonlinear method. Adaptive neuro fuzzy inference system based on fuzzy c–means clustering algorithm (ANFIS–FCM) is one of the robust artificial intelligence algorithms proved to be very successful in recognition of relationships between input and output parameters. The aim of this paper is to show the application of ANFIS–FCM in estimation of TBM performance. The model was applied to available data given in open source literatures. The results obtained show that the ANFIS–FCM model can be used successfully for estimation of the TBM performance.

The interbasin water transfer is a remedy to mitigate the negative issues of water shortage in arid and semi-arid regions. In a water transfer project the receiving basin always benefits while, the sending basin may suffer. In this study, the project of interbasin water transfer from Dez water resources system in south-west of Iran to the central part of the contrary is investigated during a drought period. To this end, a multi-objective optimization model is developed based on the Non Dominated Sorting Genetic Algorithm (NSGA-II). The optimum trade-off between the water supply benefits into and out of the Dez River basin as well as energy production is derived. Formulating the problem as a multi-objective optimization provides a better insight into the gains and losses of a water transfer project. Analyzing the case study, revealed that to reach an acceptable level of reliability for meeting the water demands it is no longer possible to generate hydropower energy with high levels of reliability.

Dynamic lot sizing problem is one of the significant problem in industrial units and it has been considered by many researchers. Considering the quantity discount in purchasing cost is one of the important and practical assumptions in the field of inventory control models and it has been less focused in terms of stochastic version of dynamic lot sizing problem. In this paper, stochastic dynamic lot sizing problem with considering the quantity discount is defined and formulated. Since the considered model is mixed integer non-linear programming, a piecewise linear approximation is also presented. In order to solve the mixed integer non-linear programming, a branch and bound algorithm are presented. Each node in the branch and bound algorithm is also MINLP which is solved based on dynamic programming framework. In each stage in this dynamic programming algorithm, there is a sub-problem which can be solved with lagrangian relaxation method. The numeric results found in this study indicate that the proposed algorithm solve the problem faster than the mathematical solution using the commercial software GAMS. Moreover, the proposed algorithm for the two discount levels are also compared with the approximate solution in mentioned software. The results indicate that our algorithm up to 12 periods not only can reach to the exact solution, it consumes less time in contrast to the approximate model.

In this paper, a method is presented for damage detection of bridges using the Enhanced Colliding Bodies Optimization (ECBO) utilizing time-domain responses. The finite element modeling of the structure is based on the equation of motion under the moving load, and the flexural stiffness of the structure is determined by the acceleration responses obtained via sensors placed in different places. Damage detection problem presented in this research is an inverse problem, which is optimized by the ECBO algorithm, and the damages in the structures are fully detected. Furthermore, for simulating the real situation, the effect of measured noises is considered on the structure, to obtain more accurate results.

This paper presents an efficient optimization procedure to find the optimal shapes of double curvature arch dams considering fluid–structure interaction subject to earthquake loading. The optimization is carried out using a combination of the magnetic charged system search, big bang-big crunch algorithm and artificial neural network methods. Performing the finite element analysis during the optimization process is time consuming. Back propagation neural network is utilized to reduce the computational burden. A real-world arch dam is considered as a numerical example to demonstrate the efficiency of the proposed method. The numerical results reveal the computational advantages of the new method for optimal design of arch dams.

Structural design optimization usually deals with multiple conflicting objectives to obtain the minimum construction cost, minimum weight, and maximum safety of the final design. Therefore, finding the optimum design is hard and time-consuming for such problems. In this paper, we borrow the basic concept of multi-criterion decision-making and combine it with Particle Swarm Optimization (PSO) to develop an algorithm for accelerating convergence toward the optimum solution in structural multi-objective optimization scenarios. The effectiveness of the proposed algorithm was illustrated in some benchmark reinforced concrete (RC) optimization problems. The main goal was to minimize the cost or weight of structures while satisfying all design requirements imposed by design codes. The results confirm the ability of the proposed algorithm to efficiently find optimal solutions for structural optimization problems.

In this paper a new method is presented for structural damage identification. First, the damaged structure is excited by short duration impact acceleration and then, the recorded structural displacement time history responses under free vibration conditions are analyzed by Continuous Wavelet Transform (CWT) and Wavelet Residual Force (WRF) is calculated. Finally, an effective damage-sensitive index is proposed to localize structural damage with a high level of accuracy. The presented method is applied to three numerical examples, namely a fifteen-story shear frame, a concrete cantilever beam and a four-story, two-bay plane steel frame, under different damage patterns, to detect structural damage either in free noise or noisy states. In addition, some comparative studies are carried out to compare the presented index with other relative indices. Obtained results, not only illustrate the good performance of the presented approach for damage identification in engineering structures, but also introduce it as a stable and viable strategy especially when the input data are contaminated with different levels of random noises.

The study aims to investigate the progressive collapse behaviour of RCC building under extreme loading events such as gas explosion in kitchen, terroristic attack, vehicular collisions and accidental overloads. The behavioural changes have been investigated and node displacements are computed when the building is subjected to sudden collapse of the load bearing elements. Herein, a RCC building designed based on Indian standard code of practice is considered. The investigation is carried out using commercially available software. The node displacement values are found under the column removal conditions and collapse resistance of building frame is studied due to increased loading for different scenarios. This simple analysis can be used to quickly analyse the structures for different failure conditions and then optimize it for various threat scenarios.

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.