In this paper, the deterioration of structural parameters namely equivalent stiffness, mass and damping due to corrosion in prestressed concrete (PC) structures using a smart probe-based piezo sensor (SPPS) via electro-mechanical impedance (EMI) technique is presented. The effectiveness of the SPPS was first demonstrated qualitatively by a change in the raw conductance signatures during the corrosion progression and different phases of corrosion (initiation, propagation, and cracking) was identified by the quantitative statistical damage indices. Assessment of material degradation under chloride-laden environment was done using equivalent structural parameters identified by SPPS from the raw admittance signatures and demonstrated the possibility to calibrate with the corrosion rates. Experimental results indicate that SPPS is very sensitive in capturing the changes during the corrosion progression and statistical metric-based method can identify the phases of corrosion. The identified structural parameters (stiffness, mass, and damping) are found to be effective in assessing the material degradation under corrosion in a realistic manner. Hence, it can be concluded that the SPPS via EMI method can be effectively employed in real-life scenarios for diagnosing the PSC structures subjected to corrosion.

The accumulation of electronic waste (E-waste) has become a significant global environmental issue, driven by the characteristics of the modern era and the ever-increasing use of electronic devices. Thus, a sustainable approach is necessary to overcome this issue. In this paper, an overview of utilizing E-waste as a sustainable aggregate in concrete production was comprehensively reviewed. The basic composition, characteristics, and production techniques were discussed. In addition to that, the colour, shape, size, aggregate crushing value, and water absorption of E-waste concrete were also discussed. Furthermore, the workability properties such as slump flow, mechanical properties (compressive strength, flexural strength, tensile strength) and thermal resistance of E-waste concrete identified by the various researchers were summarised. Based on the literature review, it is concluded that the shape and size distributions of E-waste particles greatly influenced the aggregate properties, and the percentage of replacement of E-waste aggregate affect the workability and mechanical and thermal properties of E-waste concrete.

The application of the electro-mechanical impedance (EMI) technique using piezo sensors for structural health monitoring (SHM) is based on baseline/healthy signature data, which poses serious limitations when it needs to be applied to existing structures. Therefore, the present research utilizes autoregressive integrated moving average (ARIMA), an effective time series forecasting machine learning algorithm to predict the baseline/healthy EMI data and futuristic data of reinforced concrete corroded specimens. The EMI data from the ARIMA model is validated with the experimental data, and the results obtained prove that the model could be utilized to predict the baseline and forecast the EMI corrosion data effectively. These results will aid the researchers to predict the baseline data for the existing structures and utilize the EMI technique for SHM purposes.