•To assess the damage of concrete structure subjected to freeze-thawed effects.•A method to monitor AE energy activity of core samples under uniaxial compressive loading was investigated.•The degree ...of damage was visualized using X-ray CT images.•Quantification of concrete damage was analyzed relation between AE energy parameter and accumulation of cracks.
As a detailed inspection of a concrete structure in service, core samples are usually drilled out and then mechanical properties are measured. In this study, damage estimation of freeze-thawed concrete from concrete-core samples is developed, applying acoustic emission (AE) method. By the authors, the quantitative damage evaluation of concrete has been proposed in RILEM TC −212ACD, by applying AE and damage mechanics in the compression test. In this study, characteristics evaluation of cracking damage effects in concrete mechanical properties by detected AE energy in core test. Prior to the compression test, distribution of micro-cracks in a concrete-core sample is inspected by helical X-ray computer tomography (CT). Then, freeze-thawed damaged samples are tested by the compression test with AE monitoring. Concrete-core samples were taken out of a channel structures in Hokkaido, Japan which is extremely developed cracking damage. Thus, it is demonstrated that the concentration of material damage could be evaluated by comparing geometrical characteristics of cracks with the “energy rate” of AE generation, which is analyzed by AE parameter analysis. A relation between AE energy rate, physical properties and geometrical properties of void is correlated, and thus the damage of concrete is qualitatively estimated by AE parameters.
Acoustic emission (AE) can be used to evaluate drought stress (water stress) in plants. Although bubble motion in plant vessels is suspected of being the source of AE generated by plants (burst-type ...AE), this has not yet been demonstrated experimentally. This study examined whether burst-type AE is caused by bubble motion by comparing the detected frequency with the theoretical frequency estimated based on a bubble motion equation. Four experimental treatments with different soil dry densities and water supply regimes were established, and the effect of the treatment conditions on AE characteristics was clarified. AE induced by water stress was extracted by focusing on the maximum amplitude and the centroid frequency calculated by wavelet transform analysis. The results showed that the measured frequency and the estimated frequency were almost equal, which demonstrates that the model agreed with the actual measurements and that the burst-type AE can be attributed to bubble motion. The AE induced by water stress had a smaller maximum amplitude and a higher centroid frequency than the AE detected during the water supply regime. Therefore, focusing on the centroid frequency may facilitate the extraction of AE induced by water stress.
Agricultural concrete structures are damaged by environmental factors. To maintain such structures, it is necessary to properly determine the mechanical properties and degree of damage suffered by ...concrete using core tests. In previous studies, the degree of damage has been evaluated by acoustic emissions (AE) detected in compressive stress fields. The process of fracture in damaged concrete has been evaluated by analyzing various AE parameters such as AE hits, energy, and frequency. The usefulness of many AE parameters for evaluating the fracture process is evident, but the most effective AE parameters have not yet been identified. In this study, the relationship between the stress level of concrete under compression and AE parameters was investigated using regression analysis with random forests to identify the most important AE parameters. Whether the accuracy of the regression analysis could be improved by clustering AE waves was also investigated. For this purpose, core samples, severely damaged by frost, were drilled out from a concrete headwork and subjected to compressive strength tests using the AE method. After monitoring the uniaxial compression tests, statistics for seven AE parameters were calculated for every 20 × 10
−6
increment of strain. Then, AE waves were classified into three clusters based on three parameters: peak amplitude, peak frequency, and centroid frequency. The accuracy of the regression analysis was compared using non-clustered and clustered data. The peak frequencies of cluster 1 and cluster 3 were significantly higher than that of cluster 2. This result suggests that cluster 1 and cluster 3 can be attributed to macro- or mezzo-scale damage. The regression analysis’ results showed that
R
2
was higher (0.720 as compared to 0.620), and RMSE and MAE were lower in cluster 1 and cluster 3 (high-peak-frequency clusters) than in non-clustered cases. Therefore, cluster analysis can be expected to improve the accuracy of AE testing. Finally, the importance of AE parameters using random forests was calculated. The most important parameter was determined to be rise time, and the second was the centroid frequency. These results suggest that these two parameters can be used to clarify compressive fracture behavior of damaged concrete.
The corrosion degradation of the steel sheet pile material accelerates in recent years. The extremely accelerated corrosion induces the thickness loss and decreases the mechanical characteristics. It ...is essential to evaluate the corroded situations for the design and maintenance of the infrastructures. In this study, the spatial characteristics of the corrosion loss in service steel sheet pile canals were evaluated by the geostatistics with the variogram and kriging. In experimental procedures, steel sheet pile thicknesses were measured in service drainage canals. The thickness sampling was conducted in the air, tidal, water, sludge and soil zones. In analytical procedures, the variogram analysis is conducted for modeling of the spatial structures of the corrosion loss distribution. The ordinary kriging based on the theoretical variogram interpolates the no-sampling area. As a result, the thickness profile shows the largest corrosion loss in the tidal zone. The kriging interpolation shows the acceleration of the corrosion in the flanges of the tidal zone.
This research investigates the influence of the pre-existing defects within concrete taken from the in-service irrigation structure on the strain distribution. The X-ray Computed Tomography (CT) ...technique is employed to investigate the internal concrete matrix and evaluate the defect distribution in it. The cracking system in a concrete matrix is detected as a damage type caused by the severe environment, and it is varied by the different degrees in all samples. The geometric properties of defects and their spatial location are obtained by image processing of CT images. The compression test with Acoustic Emission (AE) and Digital Image Correlation (DIC) measurements is conducted to analyze the fracture processes and acquire the damage spatial information. The AE signal descriptors are effective parameters for real-time detection and potential local damage monitoring. Moreover, the analysis of the DICM strain and displacement fields reveals the most potential fracture zones. The AE source location analysis indicated a connection between pre-existing defects and strain localization. The AE events and strain are high in the defect areas. Additionally, the amplitude and frequency of the AE events correlated with the location of the defects indicating that the structure weakness at that point leads to concentrated deformation development.
In recent years, the relationship between the durability and the degradation of concrete irrigation infrastructure has been discussed as a technical problem. In this study, the development of a ...damage estimation method for deteriorated structures will be performed by applying the acoustic emission method (AE). A quantitative damage evaluation of concrete that involves applying AE and damage mechanics in a compression test has been proposed by the authors in RILEM TC-212ACD. The procedure is named improved system for damage estimation of concrete by acoustic emission technique (i-DeCAT) and is based on evaluating detected AE energy trends in core tests. Prior to the compression test, the distribution of micro-cracks in a concrete-core sample is inspected by helical X-ray computed tomography. Then, freeze–thawed damaged samples are tested by the compression test. Thus, it is demonstrated that the concentration of material damage could be evaluated by comparing the geometrical characteristics of cracks with the “energy rate” of AE generation, which is analyzed by AE parameter analysis. A relation between AE energy rate and damage parameters is correlated, and thus the damage of concrete is qualitatively estimated by i-DeCAT.
In order to maintain serviceability and reliability of concrete structures, it is essential to assess their condition as concrete structures deteriorate in time. Cracks develop in concrete due to ...several reasons such as severe loading, environmental effects, chemical effects etc. and cause durability loss in the structure which may also lead to loss of stability. In this research, crack detection is realized by machine learning and an infrared image. The effects of infrared images on crack detection are confirmed by random forest algorithm to select useful explanatory features. Selected features are applied to random forest algorithm and neural network algorithm. Effective filters are selected as a feature selection technique to improve the accuracy. Crack detection is also conducted by U-Net with RGB and infrared images, and the detection characteristics are compared to conventional methods. The performance of two conventional machine learning methods, random forest and neural network, are evaluated based on F1 score and false positives. Applying selected features improves the accuracy of the crack detection from an infrared image. False positives decreased due to monitoring conditions and camera specifications in the infrared image. The most effective image processing filter is the blur filter for each algorithm. Comparing algorithms for crack detection using selected features, different accuracy values are obtained. U-Net enables more accurate crack detection compared to conventional methods. The number of false positives is reduced compare to conventional method. In the detection results by three algorithms, infrared image affects the balance of false negatives and false positives.
•Surface damage detection is conducted by three machine learning methods with visible and infrared images.•The effectiveness of infrared image on detectivity is evaluated by feature importances of ramdom forest algorithm.•In the detection task of crack and efflorescence, infrared images decrease false positives compared to detection by only visible image.•Blur filters improves the accuracy of damage detection by decreasing false positives caused by fine edges in cracks.•The feature importance varies between the random forest and neural network algorithms.•U-Net is able to detect crack and efflorescence with higher accuracy than conventional methods.
In recent years, the relationship between the durability and the corrosion of reinforced concrete structures has been discussed as a technical problem. In this study, the evaluation method of ...concrete's damages is proposed by acoustic emission (AE) energy under a process of compressive fracture. Concrete-core samples were the taken from a canal structure in Hokkaido, Japan, which freeze-thawed damages developed heavily. The samples were tested by the compression test with AE system. Prior to the compression test, micro-cracks of the samples were estimated by helical X-ray CT. Thus, Initial AE energy characteristics depended on the degree of damage. AE energy is an effective parameter for evaluating the degree of damage that is not fully understood by compressive strength. The damage parameter from X-ray CT images was correlated with the dynamic Young's modulus. This result suggests that the damage of concrete can be evaluated by non-destructive inspection method, such as dynamic Young's modulus.
Extreme corrosion on steel sheet piles, which includes thickness loss and pitting corrosion, has been observed in service canal walls. This extreme corrosion decreases the durability of steel sheet ...piles and causes buckling phenomena. This study aims to detect strain concentration around pitting corrosion on a steel sheet pile sample in bending stress fields with acoustic emission (AE) and digital image correlation (DIC) methods. In experimental procedures, three types of test samples were used, which had different types of thickness loss and pitting corrosion, even thickness loss, and no thickness loss. A fourpoint bending test with AE and DIC methods was conducted for these samples. In analytical procedures, AE hits, AE energy, and AE source locations in each loading process are analyzed. The DIC method are used to detect strain distribution on surfaces of the test samples. As results, AE sources and maximum principal strain concentrate around the pitting corrosion. The timeseries of the strain concentration detected using the DIC method correspond to those of the AE hits. Thus, the AE and DIC methods can be used to detect the relationship between AE hits, AE sources, and strain concentration in the bending process.
Steel sheet pile materials are primarily used for canal structure construction for irrigation and drainage of agricultural fields in Japan. Recently, accelerated corrosion of steel materials and ...buckling phenomena in pitting corrosion parts have been detected in in-service steel sheet pile canals. In this study, we focus on the buckling phenomenon of in-service steel sheet pile canals, which is detected by non-contact monitoring using digital image analysis. The buckling phenomenon index is evaluated as the inclination angle of the steel sheet pile due to deformation. As a digital image analysis, Hough transform is applied, which is a figure detection method in digital images, to detect a straight line that describes the distance and angle parameters. The angle parameter corresponds to the inclination angle index of in-service steel sheet pile with a buckling phenomenon. As a result, the maximum difference between the analyzed angles by Hough transform and the measured angles is evaluated as 0.9° after the image rotation correction. The relation of the analyzed angles by Hough transform and the measured angles of the buckling phenomenon is correlated. Therefore, the degradation of steel sheet pile canals is quantitatively evaluated by using digital image analysis with unmanned aerial vehicle monitoring. This study’s significance is non-contact and simple measurement by using a digital image which can reduce a working time and perform quick diagnosis in extensive areas.
