International Journal of Optimization in Civil Engineering
Volume:8 Issue: 3, Summer 2018

Structural damage detection is a field that has attracted a great interest in the scientific community in recent years. Most of these studies use dynamic analysis data of the beams as a diagnostic tool for damage. In this paper, a massless rotational spring was used to represent the cracked sections of beams and the natural frequencies and mode shape were obtained. For calculation of rotational spring stiffness equivalent of uncracked and cracked sections, finite element models and experimental test were used. The damage identification problem was addressed with two optimization techniques of different philosophers: ECBO, PSO and SQP methods. The objective functions used in the optimization process are based on the dynamic analysis data such as natural frequencies and mode shapes. This data was obtained by developing a software that performs the dynamic analysis of structures using the Finite Element Method (FEM). Comparison between the detected cracks using optimization method and real beam shows an acceptable agreement.

For application of the plastic analysis and design methods the control of the plastic behaviour of the structures is an important requirement. In this study, the complementary strain energy of the residual forces is considered as an overall measure of the plastic performance of the structure. Shakedown theorem for the analysis of the plastic behaviour of the laterally loaded piles is developed and applied to single vertical long pile. Limit curves are presented for the shakedown load multipliers. The formulations of the problems lead to mathematical programming which are solved by the use of nonlinear algorithm.

An effective method utilizing the differential evolution algorithm (DEA) as an optimisation solver is suggested here to detect the location and extent of single and multiple damages in structural systems using time domain response method. Changes in acceleration response of structure are considered as a criterion for damage occurrence. The acceleration of structures is obtained using Newmark method. Damage is simulated by reducing the elasticity modulus of structural members. Three illustrative examples are numerically investigated, considering also measurement noise effect. All the numerical results indicate the high accuracy of the proposed method for determining the location and severity of damage.

In this study, the complex behavior of steel encased reinforced concrete (SRC) composite beam–columns in biaxial bending is predicted by multilayer perceptron neural network. For this purpose, the previously proposed nonlinear analysis model, mixed beam-column formulation, is verified with biaxial bending test results. Then a large set of benchmark frames is provided and P-Mx-My triaxial interaction curve is obtained for them. The specifications of these frames and their analytical results are defined as inputs and targets of artificial neural network and a relatively accurate estimation model of the nonlinear behavior of these beam-columns is presented. In the end, the results of neural network are compared to some analytical examples of biaxial bending to determine the accuracy of the model.

Vibrating particles system (VPS) is a new meta-heuristic algorithm based on the free vibration of freedom system’ single degree with viscous damping. In this algorithm, each agent gradually approach to its equilibrium position; new agents are generated according to current agents and a historically best position. Enhanced vibrating particles system (EVPS) employs a new alternative procedure to enhance the performance of the VPS algorithm. Two different truss structures are investigated to demonstrate the performance of the VPS and EVPS weight optimization of structures.

In this article, by Partitioning of designing space, optimization speed is tried to be increased by GA. To this end, designing space search is done in two steps which are global search and local search. To achieve this goal, according to meshing in FEM, firstly, the list of sections is divided to specific subsets. Then, intermediate member of each subset, as representative of subset, is defined in a new list. Optimization process is started based on the new list of sections which includes subset’s representatives (global search). After some specific generations, range of optimum design is indicated for each designing variable. Afterwards, the list of sections is redefined relative to previous step’s result and based on subset of relevant variable. Finally, optimization will be continued based on the new list of sections for each designing variable to complete the generations (local search). In this regard, effect of dimension and number of subset’s members of global and local searches in proposal are investigated by optimization examples of skeletal structures. Results imply on optimization speed enhancement based on proposal in different cases proportional to simple and advanced cases of GA.

Construction industry has the highest ratio of fatality of workers in comparison with other industries. Construction safety has been always a matter of focus to control safety risks. This article presents a new flexible method of safety risk assessment by adding Hybrid Value Number (HVN) to the assessment equation. As a result of using this method, the results of assessment process will be more consistent with the project’s conditions, as well as being more trustful. It could provide a better perspective of safety risks for project managers. The most significant outcomes of this research are as follows: 1) the most influential factors which affect safety risks in building construction projects are "the proficiency and the experience of workers", "the complexity of construction technology" and "time limitation", 2) the biggest risk priority numbers belong to "Struck by falling objects" and "Falling to lower levels" hazards, 3)a necessary safety program must contain Personal Protective Equipment (PPE), safety measures and safety training, 4)Project managers can decrease 75% of total safety risks by investing less than 1.5% of construction budget on safety programs.

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.

The main focus of this research has been to investigate the effectiveness of optimal single and multiple Tuned Mass Dampers (TMDs) under different ground motions as well as to develop a procedure for designing TMD and MTMDs to be effective under multiple records. To determine the parameters of TMD and MTMDs under multiple records various scenarios have been suggested and their efficiency has been assessed. For numerical simulations, a ten-story linear shear building frame subjected to 12 real earthquakes as well as a filtered white noise record and optimum parameters of TMDs and MTMDs have been determined by solving an optimization problem using genetic algorithm (GA). The results show that when designing optimal TMD and MTMD under a specific ground motion, using the optimization procedure leads to achieve the best performance while the characteristics of the design earthquake strongly affects the performance of TMDs. Furthermore, it has been found that TMDs and MTMDs designed using only one earthquake as the design record have not worked successfully under multiple ground motions. For determining the parameters of TMDs to be effective under multiple records it has been suggested to use the mean of optimal TMDs parameters obtained using each of the design records.

This paper utilizes the Colliding Bodies of Optimization (CBO), Enhanced Colliding Bodies of Optimization (ECBO) and Vibrating Particles System (VPS) algorithms to optimize the reservoir system operation. CBO is based on physics equations governing the one-dimensional collisions between bodies, with each agent solution being considered as an object or body with mass and ECBO utilizes memory to save some historically best solutions and uses a random procedure to escape from local optima. VPS is based on simulating free vibration of single degree of freedom systems with viscous damping. To evaluate the performance of these three recent population-based meta-heuristic algorithms, they are applied to one of the most complex and challenging issues related to water resource management, called reservoir operation optimization problems. Hypothetical 4 and 10-reservoir systems are studied to demonstrate the effectiveness and robustness of the algorithms. The aim is on discovering the optimum mix of releases, which will lead to maximum benefit generation throughout the system. Comparative results show the successful performance of the VPS algorithm in comparison to the CBO and its enhanced version.