Improvement of crack detectivity for noisy concrete surface by machine learning methods and infrared images

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

Visualization and evaluation of concrete damage in-service headworks by X-ray CT and non-destructive inspection methods

The durability of concrete irrigation infrastructures decreases easily due to environmental effects, such as the freeze-thawed process. The degree of damage in concrete is, in most cases, evaluated by an unconfined compression test or ultrasonic test. The limiting states of structures are the result of a gradual accumulation of microdefects during the in-service period, which leads to the initiation and development of macroscopic cracks, loss of performance, and Failure. Therefore, to maintain such structures, it is necessary to properly determine the degree of damage by non-destructive testing. For effective maintenance and management of in-service structures, it is necessary to evaluate not only such mechanical properties as strength but also the degree of damage. Quantitative damage evaluation of concrete is proposed by applying the X-ray CT method and related non-destructive inspection, which is based on estimating cracking damage effects. In this study, the damage evaluation of concrete-core samples is investigated by X-ray CT parameters and the non-destructive parameters. The samples tested were taken from the damaged concrete headwork in Niigata, Japan, about 50 years after its construction. The geometric properties of the concrete matrix were calculated by the analysis of X-ray CT images. After X-ray CT measurement, an ultrasonic test and a resonant frequency test were performed. The dynamic modulus of elasticity was calculated by using ultrasonic pulse velocity and resonant frequency. As a result, based on the relationship between the geometric properties of the concrete matrix and the non-destructive parameters, the accumulation of damage in the samples tested was correlated with the ratio of the total area of cracks to the observation area. In damaged conditions, the high value of its ratio is demonstrated under low ultrasonic pulse velocity. These results suggest that the most sensitive components in a concrete body to the degree of damage are cracks in mortar and the ratio of the total area of cracks to the observation area. These values are affected by the internal actual cracks. Thus, the damage of concrete could be quantitatively evaluated by the geometric properties of the concrete matrix and its physical properties