In the Direction of an Artificial Intelligence-Enabled Monitoring Platform for Concrete Structures

In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson's correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment.

Piezoresistivity and AC Impedance Spectroscopy of Cement-Based Sensors: Basic Concepts, Interpretation, and Perspective

Cement-based sensors include conductive fillers to achieve a sensing capability based on the piezoresistivity phenomenon, in which the electrical resistivity changes with strain. The microstructural characterisation of cement-based sensors can be obtained using a promising non-destructive technique, such as AC impedance spectroscopy (ACIS), which has been recently used by many researchers. This paper reviews the fundamental concepts of piezoresistivity and ACIS in addition to the comparison of equivalent circuit models of cement-based sensors found in the literature. These concepts include piezoresistivity theory, factors affecting piezoresistivity measurement, resistance measurement methodology, strain/damage sensing, causes of piezoresistivity, theories of conduction, AC impedance spectroscopy theory, and the equivalent circuit model. This review aims to provide a comprehensive guide for researchers and practitioners interested in exploring and applying different techniques to self-sensing concrete.

Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete

Prominence of concrete is characterized by its high mechanical properties and durability, combined with multifunctionality and aesthetic appeal. Development of alternative eco-friendly or multipurpose materials has conditioned improvements in concrete mix design to optimize concrete production speed and price, as well as carbon footprint. Artificial neural networks represent a new and efficient tool in achieving optimal concrete mixtures according to its intended function. This paper addresses concrete mix design and the application of artificial neural networks (ANNs) for self-sensing concrete. The authors review concrete mix design methods and the development of ANNs for prediction of properties for various types of concrete. Furthermore, the authors present developments and applications of ANNs for prediction of compressive strength and flexural strength of carbon nanotubes/carbon nanofibers (CNT/CNF) reinforced concrete using experimental results for the learning process. The goal is to bring the ANN approach closer to a variety of concrete researchers and possibly propose the implementation of ANNs in the civil engineering practice.

Environment-Friendly, Self-Sensing Concrete Blended with Byproduct Wastes

Smart structures have attracted significant research attention in the last decade, mainly due to the capabilities of advanced concrete in electrical resistance-enabled self-sensing. In this study, we present a type of environment-friendly, self-sensing concrete enabled by electrical resistance. Environment-friendly, self-sensing concrete was casted with the additions of byproduct wastes (i.e., coal fly ash (FA), blast furnace slag (BOF) and red mud (RM)) at various volume fractions and cured using the conditions of 3, 7 and 28 days. The self-sensing concrete samples were experimentally tested to investigate the effects of the byproduct wastes on the mechanical and electrical properties (i.e., compressive strength and electrical resistance). In the end, parametric studies were experimentally conducted to investigate the influences of the byproduct wastes on the mechanical and electrical properties of the reported environment-friendly, self-sensing concrete.