فهرست مطالب مهران پورقلی

Infrastructures such as bridges, buildings, pipelines, marine structures, etc., play an important role in human life. Since major disasters in these structures, such as the collapse of bridges or buildings, often result in many casualties, damages, and social and economic problems, most industrialized countries allocate significant funds to monitor their health. Failure detection strategies and continuous monitoring of the structure's condition, especially after natural and manufactured disasters, make necessary measures to be taken in the early stages of failure and can reduce the cost of maintenance and the possibility of collapse. Structural health monitoring methods often provide an opportunity to reduce maintenance, repair, and retrofit costs during the structure's life cycle. Most of the structural health monitoring methods proposed and implemented to identify possible damages depend on the structure's dynamic characteristics. One of the most practical methods, which uses the results of time domain system identification to detect failure, is the damage locating vector (DLV) method. The DLV method aims to identify load combinations that result in zero strain fields for damaged members in both healthy and damaged structures. To accomplish this, we find a vector in the null space of the difference between the plasticity matrices of the two structures. The singular value analysis method is used on the plasticity difference matrix to calculate this space. The method involves applying the space vectors to the healthy structure and recording the internal stresses of the members, which are then converted into weighted normal stress (WSI) using statistical tools. The member with a lower WSI is more likely to be damaged. Since truss structures are usually used in bridges, long-span structures, as well, as a wide range of steel buildings with simple and braced frames, this research uses the covariance-based random subspace optimal method in identifying the modal characteristics, which is very efficient in low excitations, has been taken into consideration to check and monitor health during operation. To investigate the capability of the DLV method in the damage detection of these structures, a 5-story residential building with a simple steel frame was subjected to the Centro earthquake. According to the desired damage scenario, the second and fifth floors were introduced as the damaged floors in this earthquake by applying a 30 and 50% reduction in the cross-section. To account for uncertainty in the data collection, we included the mean root square of the second sensor's data in the results for sensors 3 and 5. As a result of this uncertainty, the damping error between 5 and 10% has been shown in the damaged and healthy structure. Using the method (SSI_ORT), it was observed that two DLV vectors were extracted. Further, with the increasing uncertainty of the random vibration test results, it was observed that the extraction DLVs could extract the possible damaged elements with high accuracy. Next, the effect of input and output noises on the results obtained from the DLV method was investigated. This study found that by increasing the SNR of the outputs by 15% while increasing the error of the extracted modal characteristics, the extracted DlVs also lose sufficient accuracy in diagnosing structural damage.

In stochastic subspace methods, the most important factor influencing the dynamic specifications is the dimensions of the Hankel matrix include the number of rows and columns. Using small matrix dimensions is unlikely to identify existing poles, and selecting very large dimensions not only increases the likelihood of virtual and bias poles but also increases computational costs. In this study, it is intended that the optimal dimensions of the Hankel matrix in the balanced stochastic subspace method be calculated in such a way that in addition to covering the existing poles, it also has a minimum computational cost. For this purpose, the condition number of the Hankel Matrix and Energy Indicator is used in two steps. The steps are as follows: First, calculate the optimal order of each cycle, and then use the optimal order to draw the condition number of the system matrix for different dimensions and calculate the desired dimension from its convergence. To verify the accuracy of the proposed method, the ambient vibration test of the Namin Entrance Bridge has been used. This bridge is located at the entrance of Namin city, 25 km from the center of Ardabil province, Iran, which includes two spans of 27.10m with a concrete deck. The deck of the bridge is located on beams with I sections, which are 2.5m away from each other, and the whole set of beams and deck is located on a system of foundations and piles with a diameter of 120cm. This bridge being the only entrance to the city and is exposed to various traffic loads, it was necessary to monitor the dynamic characteristics of the bridge as modal frequencies and damping ratios to evaluate the performance and ensure the health of the bridge structure. According to the numerical analysis and the length of the data (12000), the minimum order and the maximum number of cycles are 22 and 55, respectively. By diverging the curvature of the energy indicator graph, the optimal order is determined in the initial 5-12% of the singular values of cycles. For example, the maximum order of the 6th cycles was obtained, 28-62. Also, from the convergence of the maximum condition number of cycles from the 8th   cycle, the optimal dimension was selected 352. In a general summary, it can be said that the use of the energy indicator concept in finding the effective order of the stability diagram has a significant effect on reducing the uncertainty of the extracted results. So that from the three identified stable poles, two poles have been extracted in the effective-order area. Also, using the concept of conditional number to find the optimal dimension of the system was effective, so that by drawing a stability diagram for the 15th cycle, it was found that the identified modal characteristics were not significantly different from the results of the optimal cycle (8th). Finally, the extracted modal properties have an acceptable agreement with the numerical model and frequency domain decomposition method (FDD). The modal frequencies of both methods (FDDand B-SSI) have a good correlation but the damping ratios were very different. In frequency domain methods the damping ratios being very sensitive to the quality of data collection, one can expect that the results of the subspace method are closer to reality.

Finite element model is the conventional method used for static and dynamic analysis of widely used structures such as dams and bridges, since it is cheap and requires no special tools. Nevertheless, these models are not able to describe the accurate behavior of structures against dynamic loads because of simplifying assumptions used in numerical modeling process, including loading, boundary conditions and flexibility. Nowadays, modal testing is used to solve these problems. The dynamic tests used to identify civil structures’ system usually include forced, free and environment vibration tests. Considering either unknown nature of inputs or failure to measure them, some methods have been developed to analyze the results of dynamic tests which are based on measuring only output data and are known as operational modal analysis. Some of such methods are Peak Picking (PP), Frequency Domain Decomposition (FDD) and stochastic subspace methods. However, unknown nature of applied forces, the presence of environmental noise and measurement errors contribute to some uncertainties within the results of these tests. In this article, a modal analysis is presented within a stochastic subspace which is among the most robust and accurate system identification techniques. In contrast to the previous methodologies, this analysis identifies dynamic properties in optimized space instead of data space by extracting ortho-normal vector of data space. Given the optimum nature of the proposed method, more accuracy in detection and removal of unstable poles as well as high-speed analysis can be served as its advantages. In order to evaluate the proposed method in terms of civil systems detection, seismic data (being among the most real and strong environmental vibrations) and steady-state sinusoidal excitation (which is among the most precise forced vibration tests) were used. In the first step, 2001 San Fernando earthquake data were analyzed using SSI-CCA and SSI-data methods, the results of which are presented in the following. Data processing rate in the SSI-CCA method is almost twice that in SSI-data method which is because of processing in an optimum space while lowering the use of least squares method to compute system vector. Furthermore, there is one unstable pole in the results of the proposed method while 4 noisy characteristics were recognized in the results of SSI-Data method. Estimated damping ratios comprised the major difference observed in this analysis using above-mentioned two methods. Modal damping ratios estimated by the proposed method were 60% closer to the previous results when compared to those of the previous subspace method. Mode shapes of both subspace methods with MAC value of 92% and 75% for the first and the second modes, respectively, are well correlated with each other. Due to lack of access to the mode shape vectors of Alves’s method, it was not feasible to calculate the corresponding MAC value. In the following, forced vibration test results of Rajai Dam conducted by steady sine excitation in 2000 and analyzed by a method known as four spectral, are re-processed Using the SSI-CCA method. As results indicate, using the proposed method the first three modes are obtained that were not on the preliminary results. In addition, other modes are of great fit with the values of the finite element.

The dominant excitation forces are generally measurable during the forced vibration tests of structures unlike the ambient vibration tests. Not considering of input forces in the system identification is one of the main sources for error generation in the Operational Modal Analysis (OMA). Therefore, some non-structural dynamic characteristics obtained due to the excitations effects can be eliminated by considering the input forces. In this paper, a special modal analysis is presented in the subspace method that removes the excitation effect of the measured input forces from the test data using orthogonal decomposition and identifies the system with an optimal subspace method based on canonical correlation analysis (SSI-CCA). To evaluate the proposed method, the seismic response of the Pacoima dam and forced vibration test results of the Alamosa Canyon Bridge are used. Non-structural and noisy pole removal, and increased accuracy of the extracted modal properties, specially damping ratios, can be mentioned as one of the important results of this study. Four non-structural modes are identified using the SSI-Data method while the first two modes without any noises, the same as previous results, are extracted using the proposed method. In addition, the damping ratios of the Alamosa Bridge are obtained by Hammer test, which are not obtained in the previous investigations.

The presence of environmental and measurement noises and ignoring the input effects are the main sources of error in system identification using ambient vibration test results. Therefore, reducing uncertainty or noise levels from the records has always been one of the main goals of the new techniques in the field of ambient vibration. Among the modal analysis techniques, stochastic subspace identification is considered as a powerful technique. In this study, the modal analysis method based on canonical correlation analysis in stochastic subspace is presented that identifies dynamic properties in optimized space instead of data space by extracting ortho-normal vector of data space. The advantage of this method, due to the nature of canonical correlation analysis, is lower noise which results in greater accuracy in estimating modal properties. Moreover, the presented process is faster due to the smaller space of identification compared to the previous methods. To validate the proposed method, an analytical model of two-dimensional frame excited under Elcentro earthquake acceleration and also the results of ambient vibration tests carried out on the Alamosa Canyon Bridge are used. The results indicate that this method eliminates more noise than other subspace methods and moreover it is faster in solving practical problems. The computation of dynamic properties, natural frequencies and mode shapes, of Alamosa Canyon Bridge with 30 sampling sensors, space matrix size of 750 and 50 excited modes are carried out in less than 150 seconds with a quad-core 2.30 GHz processor.

In the past, design of water tanks was based on the recommendations of codes and designer's experience. Nowadays, the designs are optimized using linear and dynamic programming methods and evolutionary algorithms. Classical optimization methods not only are time consuming but also are not capable to produce more than one answer because of the complexity of imposed constraints [1]. In order to investigate the efficiency and accuracy of the modern optimization methods in comparison with classical methods, an elevated Intz tank is primarily designed based on the recommendations of Indian IS: 3370 code [2] and then optimized using genetic algorithm and sequential quadratic programming. Tanks of various sizes are optimized and effective parameters are extracted, finally the key ratios of optimum design are compared to the initial design assumptions. Genetic algorithms and their variations are based on the mechanisms of natural selection. Unlike the conventional optimization which search approaches based on gradients, the genetic algorithm (GA) works on a population of possible solutions, attempting to find a solution set that either maximizes or minimizes the value of a function of those solution values. This function is called the objective function. In GA, key tools are generation method and its associated operators. The operator will determine the rate of convergence and accuracy of genetic methods. Genetic algorithms are randomized general-purpose search techniques used for finding the best values ofthe parameters or decision-variables of existing models. Some populations of solutions may improve the value of the objective function, others may not. The ones that improve its value play a greater role in the generation of new populations of solutions than those that do not. The flowchart of GA optimization is shown in Fig. 1 [3]. The Convergence of cost function using GA and SQP methods are shown in Figs. 3 and 4, respectively. The optimal solution was achieved after seven generations and nineteen times trial and error process for the GA and SQPmethods, respectively. The SQP method provides concrete placement volume of 130 m3which in comparison with the initial volume of 230 m3 shows a 46% reduction and represents approximately 26% decrease in the formwork. On the other hand, the GA method results in 44% and 21% reduction in volume of concrete placement andformwork, respectively. It is noteworthy that the internal stresses are significantly close to their maximum values while the conditions of code regulation are satisfied in both methods. The results indicated that the GA and SQP methods are agree well in the optimized design and slightly difference, 4 percent in volume and 5 percent in area ofconcrete work, is obtained. Efficiency and accuracy of the both evolutionary optimization methods is concluded. Intz tank with 850 m3 volumes was optimized by the GA method and compared with the results of the sequential quadratic programming (SQP) method in order to indicate the efficiency and accuracy of evolutionary methods. In this study the concrete volume of the body and surface of the tank is considered as an objective function. Design constraints are divided into some groups including the behavior, geometric and stability constraints. All constraints were satisfied and the stresses and stability constraints were in acceptable ranges. Considerable amount of decreasing in objective function, 46% and 44% for GA and SQP respectively was obtained. Based on the results, both methods, GA and SQP, were reliable and flexible methods in optimizing the Intz tank.