Degradation processes in reinforced concrete structures that affect durability are partially controlled by transport of aggressive ions through the concrete microstructure. Ions are charged and the ability of concrete to hold out against transfer of ions greatly relies on its electrical resistivity. Hence, a connection could be expected between electrical resistivity of concrete and the deterioration processes such as increase in permeability and corrosion of embedded steel. Through this paper, an extensive literature review has been done to address relationship between concrete electrical resistivity and its certain durability characteristics. These durability characteristics include chloride diffusivity and corrosion of reinforcement as these have major influence on concrete degradation process. Overall, there exists an inverse or direct proportional correlation between these parameters. Evaluated results, from measuring the concrete electrical resistivity, can also be used as a great indicator to identify early age characteristics of fresh concrete and for evaluation of its properties, determination of moisture content, connectivity of the micropores, and even condition assessment of in-service structures. This paper also reviews and assesses research concerning the influential parameters such as environmental conditions and presence of steel rebar and cracks on measuring electrical resistivity of concrete. Moreover, concrete resistivity concept, application, and its various measurement techniques are introduced.

This paper describes an innovative protocol for full field mapping of a large civil structures involving effective use of Unmanned Aerial Vehicles (UAVs) to enable real time structural health monitoring. The proposed frameworks integrates UAVs, image processing and data acquisition procedures for crack detection and assessment of surface degradation. A novel approach is proposed combining hat transform and HSV thresholding technique for crack detection. In addition, grey scale thresholding is employed for the measurement of surface degradations. A Demonstration multi-rotor UAV model is developed to carry out full field inspection of civil structures and real time testing is performed in our large university campus. In order to provide sophisticated monitoring platform for users, a MATLAB Graphical User Interface (GUI) is developed to analyse real time as well as acquired images and the results are validated successfully. The obtained results confirms that, the envisaged approach is the foundation for cost effective and time compressing solution for monitoring of large civil structures

Concrete is the most widely used construction material throughout the world. Extensive experiments are conducted every year to study the physical, mechanical, and chemical properties of concrete involving a hefty amount of money and time. This work focuses on the utilization of Machine Learning (ML) algorithms to predict various concrete properties for avoiding unnecessary experimentation. In this work, six mechanical properties of concrete namely modulus of rupture, compressive strength, modulus of elasticity, Poisson’s ratio, splitting tensile strength, and coefficient of thermal expansion are estimated by applying five different ML algorithms viz. Linear Regression, Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting models on the Wisconsin concrete mixes database. Further, these ML models were evaluated to identify the most suitable model that can reliably predict the mechanical properties of concrete. The approach followed in this research was verified using the 10-fold Cross-Validation technique to eliminate training and testing split bias. The Grid Search Cross Validation method was used to find the best hyperparameters for each algorithm. Root mean squared error (RMSE) and coefficient of determination (R²) results showed that the Support Vector Machine outperformed the other models applied on the datasets. Support Vector Machine predicted the modulus of rupture of concrete after a curing time of 28 days with an R² score of 0.43, which is better than the R² scores of Random Forest and Gradient Boosting advanced algorithms by 34% and 26%, respectively.