Concrete structures with various reinforcements, such as steel bars, composite material tendons, and recently steel plates, are commonly used in civil infrastructures. When an external force overcomes the strength of the bond between the reinforcement and the concrete, bond-slip will occur, resulting in a relative displacement between the reinforcing materials and the concrete. Monitoring bond health plays an important role in guaranteeing structural safety. Recently, researchers have recognized the importance of bond-slip monitoring and performed many related investigations. In this paper, a state-of-the-art review on various smart sensors based on piezoelectric effect and fiber optic technology, as well as corresponding techniques for bond-slip monitoring is presented. Since piezoelectric sensors and fiber-optic sensors are widely used in bond-slip monitoring, their principles and relevant monitoring methods are also introduced in this paper. Particularly, the piezoelectric-based bond-slip monitoring methods including the active sensing method, the electro-mechanical impedance (EMI) method and the passive sensing using acoustic emission (AE) method, and the fiber-optic-based bond-slip detecting approaches including the fiber Bragg grating (FBG) and the distributed fiber optic sensing are highlighted. This paper provides guidance for practical applications and future development of bond-slip monitoring.

https://www.mdpi.com/1424-8220/19/5/1231/pdf

Bond-Slip Monitoring of Concrete Structures Using Smart Sensors-A Review.

To extend understanding of the bonding behavior of fiber reinforced polymer (FRP) and steel bars in self-compacting concrete (SCC), an experimental series consisting of 36 direct pull-out tests monitored by acoustic emission (AE) were performed in this paper. The test variables involved rebar type, bar diameter, embedded length, and polypropylene (PP) fiber volume content. For each test, the pull-out force and free end slip were continuously measured and compared with the corresponding AE signals. It was found that the proposed AE method was effective in detecting the debonding process between the FRP/steel bars and the hosting concrete. The AE signal strength exhibited a good correlation with the actual bond stress-slip relationship measured in each specimen. Based on the AE location technique, the invisible non-uniform distribution of bonding stress along the bar was further revealed, the initial location of damage and the debonding process were captured. Additionally, the contribution of bar-to-concrete load-bearing mechanism (chemical adhesion, friction, and mechanical interlocking) to sustain the pull-out force was effectively clarified by studying the collected signals in the frequency domain of AE methods. The experimental results demonstrate that the proposed AE method has potential to detect the debonding damage of FRP/steel bar reinforced SCC structures accurately.

https://www.mdpi.com/1424-8220/19/1/159/pdf

Investigation of Bonding Behavior of FRP and Steel Bars in Self-Compacting Concrete Structures Using Acoustic Emission Method

Evaluation of bond performance of corroded steel bars in concrete after high temperature exposure

This paper investigates the efficiency of acoustic emission (AE) technique for monitoring and assessing the bond-slip behavior and damage characteristics of deformed rebar embedded in hybrid fiber reinforced engineered cementitious composites (ECC) A series of pull-out tests were conducted on 48 prism samples with variable rebar diameters (14 mm, 20 mm, and 25 mm), embedded length (3d, 4d and 5d), and cover thickness (25d, 4d, 5d and 6d) Two AE sensors, attached on ECC surface and rebar surface respectively, were utilized to critically monitor the AE activity emitted from the ECC matrix and the interface between ECC and rebar throughout the tests The bond-slip behavior of ECC-rebar presented a more ductile performance than conventional concrete (CC)-rebar because of fiber bridging effect The damage classification and damage characteristics were clarified by measuring AE activity The debonding process of ECC-rebar could be divided into four main stages based on the slope changes of cumulative AE hits, and further refined into several substages based on the sudden rises of AE energy Besides AE cumulative parameters, the Ib-values calculated from AE signals of ECC matrix and ECC-rebar interface were analyzed and compared, which behaved more effectively in identifying the development of cracks and distinguishing the maximum bond stress Also, the AE intensity analysis was implemented to identify the micro-mechanism transformation during different bonding stages Moreover, the AE intensity results of CC-rebar obtained from previous research and ECC-rebar in this study were compared

Assessment of bond-slip behavior of hybrid fiber reinforced engineered cementitious composites (ECC) and deformed rebar via AE monitoring

Steel corrosion damage monitoring in reinforced concrete structures with the acoustic emission technique: A review

Experimental study of a pull-out test of corroded steel and concrete using the acoustic emission monitoring method

This study aims to discuss the application value of KMC algorithm optimized by heuristic method in basketball big data analysis and visual management. Because the data in basketball big data is too complicated and incomplete, the extraction of information is not direct and effective enough. Based on the metaheuristic K-Means clustering (KMC) algorithm, the weights and genetic algorithm are introduced to optimize it, and the University of California at Irvine (UCI) data set is applied to analyze the big data clustering performance of the optimized KMC algorithm. The 2018-2019 season National Basketball Association (NBA) shooting guards are selected as the research objects, and the optimized KMC algorithm is used to process the data and analyze the NBA scoring functional factors. It is found that the number of clusters increased from 2 to 16. After optimization, the Between-Within Proportion (BWP) value of the KMC algorithm only drops by 0.35, and the improved BWP (IBWP) value only drops by 0.288, which shows the smallest drop among all the algorithms. When the number of nodes is 4, the running time of the optimized KMC algorithm for processing the COVTYPE data set is 1922 s after optimization, and the running time for processing the IRIS data set is the shortest (113 s). When the number of parallel nodes is 10, the speedup ratio of the optimized KMC algorithm for processing COVTYPE data set is 4.16, and the maximal expansion rate is 0.81. The clustering accuracy of traditional KMC algorithm is 89.33%. After optimization, the clustering accuracy of KMC algorithm is 98.67%. The leader factor, offensive contribution factor, shooting stability factor, and passing ability factor in the core grouping are all at the maximum, which are 0.59, 0.51, 0.47, and 0.43, respectively. The optimized KMC algorithm has been shown to reduce the number of iterations, reduce convergence time, and improve clustering accuracy. The optimized KMC algorithm has been shown to reduce the number of iterations, reduce convergence time, and improve clustering accuracy. The conclusion of this study can provide reference basis for big data clustering and visual management.

/pdf/basketball-big-data-and-visual-management-system-under-bczmihvq.pdf

Basketball Big Data and Visual Management System under Metaheuristic Clustering

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Experimental study of a pull-out test of corroded steel and concrete using the acoustic emission monitoring method

Basketball Big Data and Visual Management System under Metaheuristic Clustering