جستجوی مقالات مرتبط با کلیدواژه "health monitoring" در نشریات گروه "عمران"

Piers are vital design elements for a bridge under seismic loading; ensuring their stability and health is crucial for the overall safety of the bridge. The most common methods available for detecting damage, primarily used in the bridge deck, are modal methods. These methods have errors in detecting damage in the structure for various reasons. On the other hand, these methods require healthy pier information. In addition to modal methods, other methods based on energy and data analysis using wavelet transform also exist, the shortcomings of which are mentioned in this article. The fundamental problem that most methods face is the existence of healthy pier information, and furthermore, parameters that should be used to detect damage must be computable or measurable. In this study, a new method is presented, using the concept of correlation, to detect the presence and location of damage with minimal error and without having healthy pier information. To this end, the tallest pier of Ramp A of the Shahid Bakri Bridge complex in Tehran was selected as the case study. Before modeling, the accuracy of the modeling method was validated, and then a precise nonlinear model was built in the OpenSees software using as-built sheets. For the first time in this article, damage in the model was created in different scenarios: reduction of stiffness in concrete cover materials, reduction of cross-sectional area of rebars at various points of the section. The location of the damage was also separately applied at three heights of the pier: 15, 25, and 35 percent of the height. To generate data on the pier, it was considered to load it with an impact load on the top of the pier. This load was applied as an impact on the deck of the pier for a very short time, and the data was collected using accelerometers at the pier height, and the correlation between each pair of consecutive sensors was calculated. Due to the high velocity of the compressive wave in concrete and the overlap that occurs in the return of the wave at both ends of the pier, data collection was performed only for about 0.001 seconds. Finally, due to the weakness of the correlation coefficient in magnifying the location of damage, using the concept of correlation, a damage index based on this concept was presented, and the capability of the presented index in detecting the presence and location of damage in various damage scenarios was evaluated. The results obtained indicate the proper performance of this index in detecting damage in various scenarios and damage intensities between 10 and 30 percent. The presented index only made an error in detecting the location of damage in cases where damage occurred in the concrete cover and rebars in small areas, but even in these cases, the presence of damage was well demonstrated. Due to limitations in sensor placement at the pier height, increasing the distance between sensors and reducing their number was also investigated, and it was observed that even with greater distances between sensors, the presented index has the capability to detect the presence and location of damage, and after finding the approximate location of damage, it is possible to identify the exact location of damage by re-sensoring in probable areas.

Introduction     Considering the importance and high cost of construction and maintenance of offshore platforms for the purpose of oil and gas extraction, and considering the fact that in case of failure, they can cause many environmental disasters, technical inspection of their structural condition is of serious importance. In addition to the high cost, this does not cover all aspects and it is very difficult to detect the failure in this case. Due to the repetitive nature of most of the environmental loads in the seas, these structures are constantly exposed to multiple and repetitive loadings. The phenomenon of fatigue is one of the effective factors in the health of these types of structures, so that during the past decades, the offshore industry has witnessed unfortunate events that often occurred due to the phenomenon of fatigue. One of the unpleasant cases can be mentioned the disaster of the Norwegian semi-submerged oil platform in the North Sea, named as the Kyland Alexandria, in which 123 people of the platform's crew lost their lives. One of the main braces connected to the base of the pontoon was completely broken and separated from the platform, causing the platform to completely overturn. The semi-submersible rig Sedo 135, which began operating in the Gulf of Mexico in 1965, suffered a fatigue failure in one of its rigs in 1967 after two years. One of the most widely used platforms in the Persian Gulf is the fixed platform of the stencil or jacket type, which is a steel structure that has braces and a deck, and foundations that are fixed on the sea floor by numerous piles. Fatigue cracks are the main failure factor in fixed jacket platforms (Ibrion et al., 2020).

Due to the increase in the age of bridges, the explanation and use of effective methods for detecting partial damage with the help of health monitoring systems are crucial since they provide the conditions for repairing the damage before the damage increases. One of the primary goals of this article is to provide an effective and efficient method for health monitoring and detect partial damage in the pier of a straight bridge using fragility curves of harmonic forces, and provide a criterion to show the correlation of fragility curves in healthy and damaged states. To achieve these goals, a complete model of a straight bridge that includes 8 piers, each 10 meters high and its deck includes 5 openings, the length of each is 15 meters, and the total length of the deck of 75 meters was modeled in the CSI Bridge software, and then, was validated. To detect the most vulnerable column for damage modeling for health monitoring operations, three earthquake records corresponding to the soil of the construction site were successively applied to the model. By examining the decrease in the slope of the moment-rotation graphs, the level of damage that occurred at the bottom of the column was detected. Finally, it was found that the middle piers were more prone to seismic damage and health monitoring operations were performed on one of the middle columns. To draw the fragility curves of harmonic forces, harmonic forces were first applied to the top of the column, and then, the rotations created at the bottom of the column were measured. Based on the obtained data and using the relevant relations, the fragility curves were drawn. The index of the maximum intensity of force, the index of rotation damage that occurred at the bottom of the column, and the defined performance level of exceedance from the linear state are the characteristics of these fragility curves. To draw the fragility curve in the damaged state, the desired earthquake (seismic) force was first applied to the bridge, and then, harmonic forces were applied to the top of the desired column. The results of this study revealed that the fragility curves of harmonic forces have been drawn well in healthy and partially damaged states according to the defined characteristics, and partial damages with different intensities have been correctly detected. To examine the correlation of the fragility curves of harmonic forces in both damaged and healthy states, two new criteria PCOMAC and PDI were introduced. The results of this article indicate that the PCOMAC criterion is very suitable for examining the correlation of fragility curves of harmonic forces.

In recent years, vibration-based damage detection has been used to assess damage to structures. In this case, damage detection is based on changing the dynamic response before and after damage. In this research, the flexibity matrix method has been used to identify damage in segmental bridge (Imam Khomeini intersection bridge). The advantage of this method is its accuracy and sensitivity to a small number of modes for large structures. The bridge was simulated in Abacus software and after applying weight and prestressing force to the cables, the frequency and modal shapes were extracted for healthy and damaged conditions. Damaged conditions included three scenarios, single (in one place) and multiple (in several places and simultaneously) on the bridge deck as well as damage to the column. Each of the above damages involved a 30% reduction in stiffness. The results showed that this method was able to detect damages in the deck. But more investigation was needed to monitor the health of the column. The accuracy of health monitoring was also assessed using two modes, which had acceptable accuracy compared to the use of 6 modes.

The problem of determining the prestressing force in the tendons of prestressed concrete structures and monitoring the non-exceedance of prestressing drops is an issue that has been addressed by many researchers over the past decades and has provided methods in this field. Today, pre-installation sensors are installed in important prestressed concrete structures to monitor prestressing loss. However, due to the unpredictability of such equipment in older structures, monitoring of these forces requires destructive or non-destructive testing but is inaccurate. Therefore, in this paper, a method is presented that without the need for these sensors and destructive tests, only by measuring static displacement, is able to detect the amount of prestressing loss in the cross-sectional tendons of a prestressed concrete beam. In this regard, an algorithm in the Python program environment based on genetic algorithm as well as modeling in the finite element analysis program is provided. The numerical example presented in this research shows that the proposed algorithm detects the values ​​of prestressing loss with good accuracy even in spite of 10% of the intentional error due to measurement. In recent years, the use of prestressing methods has become much simpler and more effective, and its materials have been optimized. Today, a high percentage of structures under construction worldwide are built using this technology, and the advance has found wide applications in the construction of office buildings, residential, commercial, parking lots, sports stadiums, concrete tanks and special structures such as piers. Therefore, in recent years, for long-term monitoring of prefabricated structures, equipment and sensors sensitive to force drop, such as fiber optic sensors and FBG sensors in the construction phase are predicted and installed in the desired locations. [13] However, since the above equipment requires a lot of money and it is not possible to use them in old structures, the need for a technique that shows the amount and location of force reduction in all tendons without using them remains. Therefore, in this paper, a method is presented that, while using the simplest tools, provides the most accurate results only by measuring static displacements under the effect of various loading scenarios and using an artificial intelligence algorithm based on genetic algorithm. The proposed method is based on computer analysis and comparison of the results of two prestressed concrete beams with the same geometry, loading and arrangement of tendons. First, a specific prestressing beam is modeled in the SAP2000 analysis program and the desired prestressing forces are applied to it, and then these forces are reduced in some of the studied tendons. This deliberate change in prestressing values ​​is considered as failure and the technique presented in this mapping tries to discover the extent and location of failure of this beam. In other words, this paper is the determination of the amount of prestressing force in prestressed concrete beams in which force measuring sensors are not predicted without the need for destructive testing and only by measuring the static displacement under load. In the form of a numerical example on a prestressed concrete beam consisting of 6 steel tendons and using a genetic algorithm, it was shown that the displacement is a function of the amount of prestressing and its location and amount of reduction by the technique used. It was correctly detected with 93% accuracy when 10% of the deliberate error due to displacement field measurement was applied. As a suggestion for future work, this research will be able to be developed in the simultaneous diagnosis of prestressing reduction and beam concrete failure.

Today, the structural health monitoring system based on various non-destructive analysis and tests has been greatly expanded in order to better performance and prevent further damage to the structure especially in areas with high seismicity. Given that, various structural health monitoring systems have been introduced. On the other hand, the discussion of maintenance costs with these methods can be helpful in choosing the most optimal method for this purpose. To this end, five new systems for health monitoring of structures, including the method of examining changes in basic information; Finite element model upgrade method, acoustic and ultrasonic method, magnetic field method and thermal field method were selected in 5 categories: management, technical, economic, human source and safety along with 20 sub-criteria in the field of maintenance. Then, through the pairwise comparison questionnaire of analytic hierarchy process method and receiving the opinions of experts in this field, different structural health monitoring systems were evaluated and prioritized to select the optimal system based on the most important criteria of building maintenance. The results showed that the structural health monitoring system by finite element model method has gained the first rank in all 5 main factors of management, technical, economic, human source and safety.

Structural control and heath monitoring is of particular importance. Columns are one of the most important structural elements, and compared to other elements, the spread of local damage, however small, can lead to premature destruction of the structure. A damaged column may have buckling instability due to an axial force less than the critical load of the healthy condition of the column; therefore, the column damage detection under the effect of axial load is important. In this paper, the column in healthy conditions and damage due to different ratios of base critical load were frequency analyzed in ABAQUS software and modal responses were extracted. Studies have shown that with increasing load, the frequency values decrease. Additionally, at the same load ratios, the healthy state frequency is higher than the damaged state frequency. Then, the curvature of the primary and secondary shapes of the vibration modes was calculated. Based on computational primary and secondary curvatures, DI1 and DI2 damage detection indices were defined. The damage detection results of curvature indices showed the optimal performance of these indices in detecting damage locations, with the difference that the DI1 index shows possible damage locations and the detection is associated with uncertainty. The results also showed that the curvature index of damage detection in one damage location is not affected by changes in damage severity in other damage locations. Additionally, the axial load has no effect on the curvature index of damage detection and this index is affected by support conditions, section area and damage severity.

In this study, a wireless piezoelectric sensor is fabricated for measurement of dynamic vibrations of steel structure. In contrast to widespread usage of piezoelectric sensors in the aerospace engineering, the application of these sensors in usual civil engineering structures is very rare. This study aims to evaluate the performance of wireless piezoelectric sensors for applications in the structural health monitoring of steel structures. To reach this aim, a wireless electrical network is firstly designed and fabricated for capturing the output voltage of the piezoelectric element. Then, the piezoelectric element and associated wireless network were installed on a prepared steel profile by using epoxy. Different mechanical loads are applied to the steel sample and the voltage induced in the piezoelectric element is saved on a computer. The mathematical relation between the amplitude of applied load and output voltage is determined. The results of experimental tests show that the fabricated piezoelectric sensor can predict the pattern of applied dynamic loads with enough accuracy.

Dams are among the most important and largest engineering structures used to supply potable and agricultural water and hydro-power as well as prevent seasonal floods. Human experience has shown that when damage occurs in these megastructures, there are irreversible financial, environmental, and fatal losses. Therefore, the issue of safety and proper performance of dams is of vital importance both in terms of designing new structures or health monitoring of the old structures. This subject is of more particular importance in the case of concrete arch dams, since in the absence of identification of small and minor damages, with the growth and spread of structural damages, a general failure may occur in these infrastructures with considerable human and financial losses. Nevertheless, concrete arch dams have been less broadly considered in the investigation, and most of the researches have included simple structures with a low-degree-of-freedom. Therefore, the main objective of this paper is to study the effects of structural damages (including the intensity and location) on the modal properties of these engineering systems. For this purpose, the modal characteristics of the structure, such as the natural frequencies and the shape-modes, are initially computed using finite element model on a concrete arch dam. Considering different scenarios for damage including, nine different locations (on different vertical and horizontal locations) are chosen. The modal characteristics of the system would be then determined before and after the occurrence of damage. In the presented research, the effects of damages at three levels of arch dam body on the modal parameters of the system are investigated. The comparison of the structural modal parameters of the intact and damaged states shows that first, the natural frequencies of the system have lower sensitivity than mode-shapes of the system, and also the damages to the top and middle are more effective than the corner zones on the modal features of the arc-dam body.

The safety of bridges as the vital arteries of cities is of great importance. There are several factors that raise concerns about the safe performance of bridges, and resolving these concerns are dependent on the appropriate decision-making of urban managers throughout the service life of bridge. These factors can be divided into two major types. The first type are factors affecting the safety of a part or whole structure of bridge and producing concerns about the bridge collapse. For easing the danger of collapse, totally or partially, a proper decision should be made to improve the behavior of a specific part of bridge at a certain time during its service life. The second type are factors influencing the safety of one or more bridge elements and increasing the life cycle costs of bridge. To prevent the growth of bridge costs due to deterioration, an appropriate plan for repair and maintenance should be implemented in order to enhance the condition of one or several elements.  In order to make the right decision, it is necessary to obtain accurate information on the condition of bridges. One of the best ways to get this information is to use bridge health monitoring. Health monitoring is the process of information acquisition from structure by installing sensors on its components and analyzing the data obtained from implemented sensors. By bridge structural health monitoring and interpreting the gathered data, the access to accurate and timely information, which is consistent with the reality of the bridge structure, is provided. Having the correct information about the bridge, the managers can decide at a lower level of risk. However, choosing specific monitoring strategy among different health monitoring systems for a bridge is a challenge that should be solved. A Quantitative index is needed to find the best technically and economically monitoring system.  The value of information (VoI) analysis is used for determining the effectiveness of monitoring information in decision-making. VoI is a method which quantifies the price of information and specifies the cost-effectiveness of decisions made on the basis of monitoring. This analysis also makes it possible to choose the most economical monitoring strategy among several alternatives. In the VoI calculation, all the uncertainties involved in the performance of a Health monitoring and probabilities of any anticipated event are considered. Thus, the decision making based on VoI is risk-based and reliable especially for important structures like bridges. In this paper, after investigating the worries and solutions for eliminating worries about the bridges in detail and introducing the applications of structural health monitoring (SHM) systems for bridges, the equations governing the VoI analysis is presented and the procedures for determining the VoI is discussed, and as an example, the VoI analysis of a bridge is discussed. According to the results of this analysis, implementing a specific amount of strain gauges with specific accuracy can provide worthy information about the bridge safety for the manager. Moreover, by the VoI analysis, the best approach for sensing system of SHM in the bridge is determined.

Localized singularities caused by changes in the stiffness or mass of the damaged region cannot be simply visible through smooth mode shape curves. However, the wavelet transform of input signal can identify the location of defects by sudden changes in the spatial variation of transformed response. The aim of this study is to present a new method for damage detection in a squarely damaged plate with element of reduced thickness with damage ratio 3% and to decrease the possible random noises that are come from different sources such as numerical solutions or physical situation. In this approach, the value of each point of mode shape data is subtracted from or is added with its symmetric point with respect to the plate’s point of symmetry (point O). The obtained value is located at its first point, again. The result of this process is a symmetrical mode shape matrix of the damaged plate, which is utilized as the input signal function in wavelet analysis.The results demonstrated that using reconstructed modal data is superior to the original modal data and they provide a better crack indicator than the result of the DWT of the original mode shape data. The effects of crack location and sampling interval are examined. The simulated results show that this approach is more effective in damage detection of plate-like structures, as the method requires the knowledge of only the response of the damaged plate as a baseline for crack detection.

Today, health monitoring of structures has been standardized in many countries. Such systems for large and complex structures are equipped and include numerous sensors. Therefore, they are not yet practical in our country due to large final expenses. The main purpose of this paper is to introduce a low-cost health monitoring algorithm for structures based on signal processing. Accordingly, only three sensors are utilized to detect damage. Since the accuracy of signal processing method can affect the results of damage detection, in the first part of the paper, five signal processing methods are investigated. Among these procedures two methods are older and have widely used in damage detection. The three others are more recent and are fully investigated in civil engineering. In the second part, a new damage detection method is proposed. This method is a combination of signal processing methods by synchrosqeezed wavelet transform, clustering, and regression with autoassociative artificial neural networks. For this reason, data from Yonghe bridge is utilized which is recorded based on real vibration of the bridge. Results show that the proposed signal processing method is capable to effectively extract signal features. Also the damage detection method is capable to detect damage with acceptable accuracy