Structural health monitoring (SHM) and damage assessment of civil engineering infrastructure are complex tasks. Structural health and strength of structures are influenced by various factors, such as the material production stage, transportation, placement, workmanship, formwork removal, and concrete curing. Technological advancements and the widespread availability of Wi-Fi networks has resulted in SHM shifting from traditional wire-based methods to Internet of Things (IoT)-based real-time wireless sensors. Comprehensive structural health assessment can be performed through the efficient use of real-time test data on structures obtained from various types of IoT sensors, which monitor several health parameters of structures, available on cloud-based data storage systems. The sensor data may be subsequently used for various applications, such as forecasting masonry construction deterioration, predicting the early-stage compressive strength of concrete, forecasting the optimum time for the removal of formwork, vibration and curing quality control, crack detection in buildings, pothole detection on roads, determination of the construction quality, corrosion diagnosis, identification of various damage typologies and seismic vulnerability assessment. This review paper summarizes the applications of the wireless IoT technology in the monitoring of civil engineering infrastructure. In addition, several case studies on real structures and laboratory investigations for monitoring the structural health of civil engineering constructions are discussed. 

Structural health monitoring of civil engineering structures by using the internet of things: A review

This study investigates the feasibility of smart aggregate (SA) sensors and their optimal locations for impedance-based damage monitoring in prestressed concrete (PSC) anchorage zones. Firstly, numerical stress analyses are performed on the PSC anchorage zone to determine the location of potential damage that is induced by prestressing forces. Secondly, a simplified impedance model is briefly described for the SA sensor in the anchorage. Thirdly, numerical impedance analyses are performed to explore the sensitivities of a few SA sensors in the anchorage zone under the variation of prestressing forces and under the occurrence of artificial damage events. Finally, a real-scale PSC anchorage zone is experimentally examined to evaluate the optimal localization of the SA sensor for concrete damage detection. Impedance responses measured under a series of prestressing forces are statistically quantified to estimate the performance of damage monitoring via the SA sensor in the PSC anchorage.

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone

Early-age concrete strength monitoring using smart aggregate based on electromechanical impedance and machine learning

Smart aggregates (SAs) are often formed by embedding lead zirconate titanate (PZT) patches into concrete or marble blocks. They not only have the advantages of low cost, quick response, high reliability, and long service life, but also possess comprehensive actuating and sensing abilities, and have been widely used in structural health monitoring in the field of civil engineering. However, due to the plate-like geometry of the PZT patch and the limited number of layers, SAs have a relatively short sensing range. To solve this issue, a new type of SA using piezoelectric stacks was developed. Theoretical modeling of this new transducer was established, and prototypes were fabricated. Comparisons between the theoretical predictions and the experimental results are presented, and good agreement can be found. The effects of the key parameters, including the total height of the specimen, the elastic modulus of the cement, the radius of the piezoelectric stack, the thickness of the piezoelectric layer, and the number of piezoelectric layers in the piezoelectric stack, on the electromechanical properties were analyzed, and the guidelines for optimal design were presented. In addition, the improved and the traditional SAs were used to monitor the water content in soil specimens based on the electromechanical impedance technique. The results showed that the improved SAs using piezoelectric stacks are more sensitive than the traditional ones, and have good potential in structural health monitoring in the field of civil engineering.

Modeling and electromechanical performance of improved smart aggregates using piezoelectric stacks

This research evaluated the feasibility of using iron ore tailings (IOTs) to produce functional conductive composite materials as cement-based sensors based on carbon fiber (CF) reinforced cement composite. In this study, recycled IOTs are used as an alternative material to natural fine aggregate (0%, 30%, and 60%). Nine mixtures were produced. The effects of CF and IOTs content on mechanical properties and electrical conductivity were investigated; Based on the digital image correlation (DIC) system, the stress sensing ability of the specimen was evaluated and the strain field distribution was extracted; According to the results of compressive stress sensing, a predictive compressive stress model was proposed, and the ratio of fractional changes of resistivity (FCR) with unit stress and CF content were fitted; The pore size distribution was studied using nuclear magnetic resonance (NMR) technology; Besides, scanning electron microscope (SEM) and X-ray diffraction (XRD) were used to analyze the microscopic morphology and crystal phase composition. The experimental results revealed that the appropriate IOTs content could improve the pore size distribution and the mechanical properties, which is a maximum increase of 28.12% compared with the plain cement (PC). The DIC strain field distribution shows that the excessive IOTs could increase the brittleness of the matrix, and XRD crystal phase analysis showed that the calcium silicate hydrate (CSH) gel is reduced and the bond with CF is weakened. The addition of CF significantly reduces the resistivity of the composites by an order of magnitude, and the self-sensing capability can be increased by 3 times. The compression stress sensitivity has a linear relationship with the amount of CF, which can be used to predict the compression stress sensing efficiency. It is expected to develop industrial waste IOTs as an effective conductive filler to promote the wider use of CF conductive composite materials.

Influence of iron ore tailings by-product on the mechanical and electrical properties of carbon fiber reinforced cement-based composites

At present, the recycled aggregate concrete (RAC) technology has served as an environment-friendly means to dispose the waste concrete. This paper mainly investigates the feasibility of the early-age strength monitoring of the RAC using the electro-mechanical impedance (EMI) method. Both the compressive test and EMI test utilizing the embedded piezoelectric smart aggregate (SA) transducers were conducted on the specimens with different recycled coarse aggregate (RCA) replacement ratios and loading ages. The compressive test results show the early-age strength of the RAC develops more slowly on the whole as the RCA replacement ratio increases. In addition, the EMI test results as well as the theoretical analysis show that the conductance resonant frequency (CRF) of the SA generally rises as the age goes on. By regression analysis, the linear relationship between the early-age strength of the RAC and the CRF increment was established. It validates the applicability of using the EMI method to monitor the early-age strength of the RAC in engineering practice.

Xiaobin Hu

Papers

Early-age strength monitoring of the recycled aggregate concrete using the EMI method