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The aim of this study is to propose a new detection method for determining the damage locations in pile foundations based on deep learning using acoustic emission data. First, the damage location is simulated using a back propagation neural network deep learning model with an acoustic emission data set acquired from pile hit experiments. In particular, the damage location is identified using two parameters: the pile location (PL) and the distance from the pile cap (DS). This study investigates the influences of various acoustic emission parameters, numbers of sensors, sensor installation locations, and the time difference on the prediction accuracy of PL and DS. In addition, correlations between the damage location and acoustic emission parameters are investigated. Second, the damage step condition is determined using a classification model with an acoustic emission data set acquired from uniaxial compressive strength experiments. Finally, a new damage detection and evaluation method for pile foundations is proposed. This new method is capable of continuously detecting and evaluating the damage of pile foundations in service.

The acoustic emission (AE) technique has been widely used to monitor damage for concrete structures in underground space. To analyze the damage status successfully, it is important to understand the characteristics of AE parameters during failure for concrete-based-underground structures. The AE parameters are related to the target material characteristics (i.e., sand content ratio) and failure steps (i.e., stress levels) of concrete specimens. In this study, the concrete specimens were prepared with different sand content ratio. Uniaxial compressive tests were performed for the specimens with AE monitoring to investigate the characteristics of AE parameters in the time domain (i.e., count, energy, and amplitude) and frequency domain (i.e., initial frequency and peak frequency). The experimental results were analyzed with the sand content ratio and failure step (1–4) in order to determine the suitable AE parameter as a monitoring index. The most suitable parameter was shown as the cumulative energy parameter of AE with high sensitivity.

Elastic wave based technology (e.g., acoustic emission or micro seismic sensors) has been widely used to monitor the stability of underground structures or rock failure. To achieve successful measurement, it is important to select an appropriate coupling material for the installation of sensors that are embedded in holes bored into the wall of a structure. The characteristics of the coupling material are related to its wave propagation ability (i.e., wave velocity and damping ratio), setting ability in the borehole (i.e., bleeding), and its workability (i.e., viscosity) in relation to being able to fill the holes tightly with the sensors. In this study, micro cement, Portland cement, and gypsum were the materials considered to couple with sensors in the borehole. Laboratory tests were performed to evaluate the characteristics of the coupling material according to the water-cement ratio (w/c, range 0.5–2.0). The test results show that the propagation ability and setting ability tend to decrease with increasing w/c ratio; on the other hand, workability benefits from an increasing w/c ratio. In addition, the characteristics of the coupling materials were analyzed to suggest a material with an optimized w/c ratio appropriate for a target test-bed site. The most suitable coupling recipe was found to be micro cement with a w/c ratio of 1.0. This study provides information about coupling material characteristics that should be useful for determining a suitable coupling recipe for field applications.

Acoustic emission (AE) monitoring has gained significant interest as a promising method for monitoring of changes in structural integrity and durability. Long-term AE monitoring needs to detect and distinguish crack signals from ambient noise (or dummy) signals; however, it is still a daunting task which currently limits field implementation of the AE method. Herein, we explore the feasibility of using convolutional neural network (CNN) models to detect AE crack signals from ambient signals. The trained models are validated both with noise-embedded synthesized signals and with upscaled physical model experiments simulating earthquake loading to a scaled model foundation by using a large-scale shaking table. The 2D CNN model trained the laboratory-synthesized signal sets effectively captured the crack and crack-free signals in all cases including the upscaled physical model experiments. This study presents a simple but robust CNN model for pre-filtering of crack signals and a novel training method for enhanced accuracy, which can be applied for real-time structural health monitoring of concrete-based structures.

Detecting and monitoring the damage locations of in-service reinforced concrete (RC) structures is difficult as they exhibit more complex geometries and consist of composite materials. In this study, a new method for detecting the source location of acoustic emission (AE) signals in RC structures using machine learning was introduced and tested on an in-service RC column. An impulse response test was conducted using an AE testing system to gather data regarding the source locations. Meanwhile, the source locations on the test column were generated through manual hits. The corresponding AE data were collected using a AE data acquisition system. The significant AE parameters were determined using neighborhood component analysis for feature selection to obtain a dataset for machine learning. Further, a support vector machine classifier was adopted to classify the source locations of the columns for three sensor groups. Furthermore, the source locations were predicted using feedforward backpropagation neural networks. This study validated the detectability of AE signals in RC columns and proposed a novel detection method, which can be employed to perform continuous nondestructive tests on in-service RC columns.

Acoustic emission (AE) has garnered significant interest as a promising way to detect the early-stage development of internal cracks and damage in underground and geotechnical structures, associated with natural disasters. Meanwhile, AE source localization techniques that can identify the damage location in a piled-raft foundation (PRF) are premature because of its complex geometry, although the PRF is a widely used deep foundation type for high-rise buildings. In this study, we propose an integrated approach to localize AE sources in the PRF by using the modified Akaike information criterion (AIC) method and examine its accuracy to mark with pile zones. We performed a series of experiments on a scaled PRF model at a ratio of 1:50, composed of one raft and 25 piles. The results demonstrate that the combined approach with the modified AIC method and the Simplex method can localize the AE source zones with good accuracy, greater than 95% on average. The suggested two-stage AIC picker shows accurate onset time determination, and hence, it significantly improves the accuracy, particularly effective for the signals with low signal-to-noise ratios. The approach exploiting the two-stage AIC picker can be readily used for automated real-time AE monitoring to detect crack generation and its location in buried foundations that cannot be inspected visually.

As one of the non-destructive testing (NDT) methods, acoustic emission (AE) can be widely applied to the field of engineering and applied science owing to its advantageous characteristics. In particular, the AE method is effectively applied to monitor concrete structures in civil engineering. For this technology to be employed in a monitoring system, it is necessary to investigate the propagation characteristics of the AE in structures. Hence, this study investigates the characteristics of AE in concrete structures to evaluate the field applicability of AE monitoring systems. To achieve this goal, experiments employing an AE system are conducted for concrete structures 20 &times, 0.2 &times, 1.2 m in length, width, and height, respectively, to explore the AE parameters according to the impact energy. Among all AE parameters, absolute energy is determined to be most sensitive factor with respect to the impact energy. In addition, the attenuation effect of the AE wave is quantitatively evaluated according to the wave propagation distance. Moreover, the concept of effective distance is newly suggested based on the experimental results. The effective distance is shown to increase as the impact energy increases, although the increased effective distance is limited because the damaged AE signal is of high frequency. This study helps improve the field applicability of AE monitoring systems by suggesting suitable AE sensor spacing, which contributes to promote the practice of technology.