This letter presents an automated acoustic sensing device for rapid detection of delamination in concrete. The device consists of ball-chains for continual impact excitation and multi-channel microphones for acoustic sensing. A ball-chain is formed by multiple metal balls connected by flexible ropes and is dragged on concrete surface to excite vibration of delaminations. Compared to the conventional chain drag test, the ball-chain generates acoustic signals with higher signal-to-noise ratio (S/N) because the balls give isolated but continual impacts on concrete surface during dragging. The proposed method was validated on a concrete specimen with artificial delaminations.

In this study, an automated acoustic scanning system was developed for rapid delamination detection on concrete bridge decks. A new ball-chain impact source was designed by combining the advantages of chain-drag (rapid testing speed) and impact-echo tests (analysis in the frequency domain). Conventional steel link chains and the new ball chains were investigated as acoustic excitation sources. Acoustic signals were processed by short-time Fourier transform (STFT) in the frequency range 0.5–5 kHz. Compared with the conventional chain-drag test, the ball-chain results show better signal-to-noise ratio (S/N), higher sensitivity, and repeatability to delamination identification. The automated scanning system includes excitation sources (ball chains), acoustic sensors (microphones), a geographic positioning system (GPS), data acquisition (oscilloscope), and a signal-processing algorithm. Acoustic scanning results are integrated with positioning data to generate an image of the scanned area in a map view. The system was validated in the field on a concrete bridge deck and was found to show satisfactory accuracy, efficiency, and repeatability.

This paper discusses the challenges in characterizing electromagnetic (EM) waves propagating through inhomogeneous media, such as reinforced cement concrete and hot mix asphalt. Understanding the EM properties of materials, including their dielectric constant, conductivity, and magnetic permeability, is crucial to analyzing the behavior of these waves. The focus of this study is to develop a numerical model for EM antennas using the finite difference time domain (FDTD) method, and to gain a deeper understanding of various EM wave phenomena. Additionally, we verify the accuracy of our model by comparing its results with experimental data. We analyze several antenna models with different materials, including the absorber, high-density polyethylene and perfect electrical conductors, to obtain an analytical signal response that is verified against the experimental response. Furthermore, we model the inhomogeneous mixture of randomly distributed aggregates and voids within a medium. We verify the practicality and reliability of our inhomogeneous models using experimental radar responses on an inhomogeneous medium.

This study presents a developed ultrasonic water level detection (UWLD) system with an energy-efficient design and dual-target monitoring. The water level monitoring system with a non-contact sensor is one of the suitable methods since it is not directly exposed to water. In addition, a web-based monitoring system using a cloud computing platform is a well-known technique to provide real-time water level monitoring. However, the long-term stable operation of remotely communicating units is an issue for real-time water level monitoring. Therefore, this paper proposes a UWLD unit using a low-power consumption design for renewable energy harvesting (e.g., solar) by controlling the unit with dual microcontrollers (MCUs) to improve the energy efficiency of the system. In addition, dual targeting to the pavement and streamside is uniquely designed to monitor both the urban inundation and stream overflow. The real-time water level monitoring data obtained from the proposed UWLD system is analyzed with water level changing rate (WLCR) and water level index. The quantified WLCR and water level index with various sampling rates present a different sensitivity to heavy rain.

This study presents an advanced signal analysis model for internal defect identification in bridge decks using impact echo field testing data designed to mitigate signal noise and the variability encountered during real-world inspections. Field tests were conducted on a concrete bridge deck utilizing an automated inspection system, systematically capturing impact-echo signals across multiple scanning paths. The large volume of field-acquired data poses significant challenges, particularly in identifying defects and isolating clean signals and suppressing noise under variable environmental conditions. To enhance the accuracy of defect detection, a deep learning framework was designed to refine critical signal parameters, such as signal duration and the starting point in relation to the zero-crossing. A convolutional neural network (CNN)-based classification model was developed to categorize signals into delamination, non-delamination, and insignificant classes. Through systematic parameter tuning, optimal values of 1 ms signal duration and 0.1 ms starting time were identified, resulting in a classification accuracy of 88.8%. Laboratory test results were used to validate the signal behavior trends observed during the parameter optimization process. Comparison of defect maps generated before and after applying the CNN-optimized signal parameters revealed significant enhancements in detection accuracy. The findings highlight the effectiveness of integrating advanced signal analysis and deep learning techniques with impact-echo testing, offering a robust non-destructive evaluation approach for large-scaled infrastructures such as bridge deck condition assessment.