Showing papers by "An Chen published in 2019"

Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array

Abstract: Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens.

Model-assisted validation of a strain-based dense sensor network

Abstract: Recent advances in sensing are empowering the deployment of inexpensive dense sensor networks (DSNs) to conduct structural health monitoring (SHM) on large-scale structural and mechanical systems. There is a need to develop methodologies to facilitate the validation of these DSNs. Such methodologies could yield better designs of DSNs, enabling faster and more accurate monitoring of states for enhancing SHM. This paper investigates a model-assisted approach to validate a DSN of strain gauges under uncertainty. First, an approximate physical representation of the system, termed the physics-driven surrogate, is created based on the sensor network configuration. The representation consists of a state-space model, coupled with an adaptive mechanism based on sliding mode theory, to update the stiffness matrix to best match the measured responses, assuming knowledge of the mass matrix and damping parameters. Second, the physics-driven surrogate model is used to conduct a series of numerical simulations to map damages of interest to relevant features extracted from the synthetic signals that integrate uncertainties propagating through the physical representation. The capacity of the algorithm at detecting and localizing damages is quantified through probability of detection (POD) maps. It follows that such POD maps provide a direct quantification of the DSNs’ capability at conducting its SHM task. The proposed approach is demonstrated using numerical simulations on a cantilevered plate elastically restrained at the root equipped with strain gauges, where the damage of interest is a change in the root’s bending rigidity.

Detection and Monitoring of Cracks in Reinforced Concrete Using an Elastic Sensing Skin

Abstract: A sensing skin has been employed to detect and monitor cracks in reinforced concrete specimens. This sensing skin is constituted of a flexible electronic termed soft elastomeric (SEC) capacitor, which detects a change in strain through changes in capacitance. The SEC is a low cost and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel bridges. The sensor is highly elastic and as such offers a unique capability to detect and monitor the growth of cracks in structural elements. In this study, an array of surfacedeployed SECs was used to detect and locate bending-induced cracks. To validate the proposed approach, an experimental campaign was conducted using reinforced concrete beams. Threepoint bending tests were conducted on two small-scale reinforced concrete beams. Different configurations of SEC arrays were used on the two specimens to assess the capacity and limitation of the proposed approach. Results show that the sensing skin was capable of detecting and localizing cracks that formed in both specimens. Additionally, the sensor is shown to offer a good signal-to-noise ratio and thus could represent a cost-effective alternative to current sensing technologies for the monitoring of cracks in concrete structures.

Surface sensing-based technique for nondestructive evaluation

Abstract: The use of sensor networks for structural health monitoring purposes has gained popularity due to advances in electronics enabling the deployment of cost-effective solutions. However, linking signal to condition evaluation is still a difficult task, and metrics must be developed to validate the performance of a given sensor network at conducting its as-designed structural health monitoring task. In this paper, we present a model-assisted sensor network validation strategy. The strategy consists of constructing a physical surrogate model to perform numerical investigations of sensor network performance under uncertainty. The update of the physical surrogate provides spatiotemporal data enabling condition evaluation. A metric inspired by probability of detection theory is developed to quantify performance. We demonstrate the methodology to validate the performance of a novel strain-based sensor network.