The purpose of this study is to facilitate damage detection and health monitoring in concrete bridge girders without the need for visual inspection while minimizing field measurements. Simple span beams with different geometry, material and cracking parameters were modeled using Abaqus finite element analysis software to obtain stiffness values at specified nodes. The resulting databases were used to train two Artificial Neural Networks (ANNs). The first network (ANN1) solves the forward problem of providing a health index parameter based on predicted stiffness values. The second network (ANN2) solves the inverse problem of predicting the most probable cracking pattern. For the forward problem, ANN1 had the geometric, material and cracking parameters as inputs and stiffness values as outputs. This network provided excellent prediction accuracy measures (R² > 99%). ANN2 had the geometric and material parameters as well as stiffness values as inputs and cracking parameters as outputs. This network provided less accurate predictions compared to ANN1, however, ANN2 results were reasonable considering the non-uniqueness of this problem's solution. An experimental verification program will be conducted to qualify the effectiveness of the method proposed. This test program is described in details in the present paper.
{"title":"An Artificial Intelligence Approach to Objective Health Monitoring and Damage Detection in Concrete Bridge Girders","authors":"Ahmed H. Al-Rahmani, H. Rasheed, Y. Najjar","doi":"10.14359/51687081","DOIUrl":"https://doi.org/10.14359/51687081","url":null,"abstract":"The purpose of this study is to facilitate damage detection and health monitoring in concrete bridge girders without the need for visual inspection while minimizing field measurements. Simple span beams with different geometry, material and cracking parameters were modeled using Abaqus finite element analysis software to obtain stiffness values at specified nodes. The resulting databases were used to train two Artificial Neural Networks (ANNs). The first network (ANN1) solves the forward problem of providing a health index parameter based on predicted stiffness values. The second network (ANN2) solves the inverse problem of predicting the most probable cracking pattern. For the forward problem, ANN1 had the geometric, material and cracking parameters as inputs and stiffness values as outputs. This network provided excellent prediction accuracy measures (R² > 99%). ANN2 had the geometric and material parameters as well as stiffness values as inputs and cracking parameters as outputs. This network provided less accurate predictions compared to ANN1, however, ANN2 results were reasonable considering the non-uniqueness of this problem's solution. An experimental verification program will be conducted to qualify the effectiveness of the method proposed. This test program is described in details in the present paper.","PeriodicalId":191674,"journal":{"name":"\"SP-298: Advanced Materials and Sensors Towards Smart Concrete Bridges: Concept, Performance, Evaluation, and Repair\"","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116780247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The most advanced method of investigating the performance of a structure is to continuously track the strain, deflection, and acceleration by analysing data collected from a series of wireless sensors installed on the structural member. Before analysing the data, it is important to assure the reliability of the data by verifying that all sensors are working properly. For an instance, in the reinforced concrete structure sensors are attached to the reinforcement bars and might be destroyed while pouring the concrete. Besides, sensors might malfunction due to excessive variation of temperature, load, or any other causes. Data-driven and structural models-based are two damage detection techniques in civil structures. In this study, the data driven method, a direct approach to damage assessment, was followed; this approach does not require structural modeling, such as finite element analysis. In this method, the existence of damage and its location are interpreted by pattern matching of the data series at different time ranges. The objective of this study was to develop new techniques to detect defective sensors based on the pattern matching method that included the Auto Regression Xeogeneous model. As a case study, Portage Creek Bridge was selected, located in British Colombia, Canada. Data sets of strain and temperature gages were downloaded from a database connected to the instrumented pier of the bridge and filtered and normalized continuously. The condition of a set of sensors installed in the pier was determined, using a method developed based on the concept of the sequential and binary search techniques. Using sensitivity analyses of the developed models, defective sensors were detected by pattern matching of simulated and measured or real data.
研究结构性能的最先进方法是通过分析安装在结构构件上的一系列无线传感器收集的数据，连续跟踪应变、挠度和加速度。在分析数据之前，重要的是要通过验证所有传感器正常工作来确保数据的可靠性。例如，在钢筋混凝土结构中，传感器附着在钢筋上，在浇筑混凝土时可能会被破坏。此外，传感器可能由于温度、负载或任何其他原因的过度变化而发生故障。数据驱动和结构模型驱动是土木结构损伤检测的两种技术。在本研究中，采用数据驱动法，一种直接的损伤评估方法;这种方法不需要结构建模，比如有限元分析。该方法通过对不同时间范围内的数据序列进行模式匹配来解释损伤的存在和位置。本研究的目的是开发基于模式匹配方法的检测缺陷传感器的新技术，其中包括自回归均匀模型。作为案例研究，选择了位于加拿大不列颠哥伦比亚省的Portage Creek Bridge。从连接到桥梁仪表墩的数据库中下载应变和温度测量数据集，并进行连续滤波和归一化。采用一种基于序贯搜索和二分搜索概念的方法，确定了安装在桥墩上的一组传感器的条件。利用所建立模型的灵敏度分析，通过模拟数据与实测数据或实际数据的模式匹配，检测出传感器的缺陷。
{"title":"A Pattern-Based Method for Defective Sensors Detection in an Instrumented Bridge","authors":"M. Islam, A. Bagchi, A. Said","doi":"10.14359/51687088","DOIUrl":"https://doi.org/10.14359/51687088","url":null,"abstract":"The most advanced method of investigating the performance of a structure is to continuously track the strain, deflection, and acceleration by analysing data collected from a series of wireless sensors installed on the structural member. Before analysing the data, it is important to assure the reliability of the data by verifying that all sensors are working properly. For an instance, in the reinforced concrete structure sensors are attached to the reinforcement bars and might be destroyed while pouring the concrete. Besides, sensors might malfunction due to excessive variation of temperature, load, or any other causes. Data-driven and structural models-based are two damage detection techniques in civil structures. In this study, the data driven method, a direct approach to damage assessment, was followed; this approach does not require structural modeling, such as finite element analysis. In this method, the existence of damage and its location are interpreted by pattern matching of the data series at different time ranges. The objective of this study was to develop new techniques to detect defective sensors based on the pattern matching method that included the Auto Regression Xeogeneous model. As a case study, Portage Creek Bridge was selected, located in British Colombia, Canada. Data sets of strain and temperature gages were downloaded from a database connected to the instrumented pier of the bridge and filtered and normalized continuously. The condition of a set of sensors installed in the pier was determined, using a method developed based on the concept of the sequential and binary search techniques. Using sensitivity analyses of the developed models, defective sensors were detected by pattern matching of simulated and measured or real data.","PeriodicalId":191674,"journal":{"name":"\"SP-298: Advanced Materials and Sensors Towards Smart Concrete Bridges: Concept, Performance, Evaluation, and Repair\"","volume":"41 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122714054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fiber-Optic Sensors (FOSs) are being introduced in structural health monitoring of bridges and other structures as an alternative to conventional sensors such as electrical strain gauges and vibrating wires. Advantages of FOS, from a materials point of view, include resilience and durability. This study examines the viability of using Osmos FOSs to monitor corrosion-damage in scaled-down reinforced concrete columns. The test variables include the corrosion level, different rebar diameters and concrete covers. Five circular reinforced concrete (RC) columns were cast. The columns were 300mm (12 inch) in diameter by 900 mm (36 inch) long. Each column was reinforced longitudinally with 6 rebars (15M or 20 M or 25 M) and 10M stirrups were provided at 200mm (8 inch) o/c. The concrete cover was 30mm or 45mm or 60mm (1.25 inch or 1.75 inch or 2.15 inch). Accelerated corrosion technique was used to corrode the longitudinal rebars in the columns up to 10% mass loss. The columns were instrumented with Osmos FOSs that were externally mounted around the column’s circumference to monitor the lateral deformation due to corrosion. In addition, corrosion crack widths on the column face were monitored during corrosion exposure. The test results showed that Farady’s law prediction works well for low corrosion levels (up to 5% mass loss) but not for high corrosion levels (10% mass loss) and that it becomes un-conservative as the rebar diameter increases. Corrosion expansion measured based on the Osmos FOS readings and the summation of crack widths across the circumference of the column showed very good correlation. It was found that the corrosion expansion increases as the rebar size increases at any corrosion level and that the corrosion expansion increases as the concrete cover increases at high corrosion level. Therefore, based on the findings of this study, Osmos FOSs can be used in the assessment and monitoring corrosion of steel reinforcement in reinforced concrete columns.
{"title":"Using Osmos FOS to Assess Corrosion Damage in RC Columns","authors":"N. Wahab, K. Soudki","doi":"10.14359/51687085","DOIUrl":"https://doi.org/10.14359/51687085","url":null,"abstract":"Fiber-Optic Sensors (FOSs) are being introduced in structural health monitoring of bridges and other structures as an alternative to conventional sensors such as electrical strain gauges and vibrating wires. Advantages of FOS, from a materials point of view, include resilience and durability. This study examines the viability of using Osmos FOSs to monitor corrosion-damage in scaled-down reinforced concrete columns. The test variables include the corrosion level, different rebar diameters and concrete covers. Five circular reinforced concrete (RC) columns were cast. The columns were 300mm (12 inch) in diameter by 900 mm (36 inch) long. Each column was reinforced longitudinally with 6 rebars (15M or 20 M or 25 M) and 10M stirrups were provided at 200mm (8 inch) o/c. The concrete cover was 30mm or 45mm or 60mm (1.25 inch or 1.75 inch or 2.15 inch). Accelerated corrosion technique was used to corrode the longitudinal rebars in the columns up to 10% mass loss. The columns were instrumented with Osmos FOSs that were externally mounted around the column’s circumference to monitor the lateral deformation due to corrosion. In addition, corrosion crack widths on the column face were monitored during corrosion exposure. The test results showed that Farady’s law prediction works well for low corrosion levels (up to 5% mass loss) but not for high corrosion levels (10% mass loss) and that it becomes un-conservative as the rebar diameter increases. Corrosion expansion measured based on the Osmos FOS readings and the summation of crack widths across the circumference of the column showed very good correlation. It was found that the corrosion expansion increases as the rebar size increases at any corrosion level and that the corrosion expansion increases as the concrete cover increases at high corrosion level. Therefore, based on the findings of this study, Osmos FOSs can be used in the assessment and monitoring corrosion of steel reinforcement in reinforced concrete columns.","PeriodicalId":191674,"journal":{"name":"\"SP-298: Advanced Materials and Sensors Towards Smart Concrete Bridges: Concept, Performance, Evaluation, and Repair\"","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126951210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite that steel fiber concrete (SFC) has been used in concrete structures during more than 50 years there is still a lack of practical recommendations. In Sweden, SFC has been used in concrete o ...
{"title":"Swedish Recommendations for Steel Fiber Concrete Overlays","authors":"J. Silfwerbrand","doi":"10.14359/51687078","DOIUrl":"https://doi.org/10.14359/51687078","url":null,"abstract":"Despite that steel fiber concrete (SFC) has been used in concrete structures during more than 50 years there is still a lack of practical recommendations. In Sweden, SFC has been used in concrete o ...","PeriodicalId":191674,"journal":{"name":"\"SP-298: Advanced Materials and Sensors Towards Smart Concrete Bridges: Concept, Performance, Evaluation, and Repair\"","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132445380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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