This paper presents a review on combining NDT techniques, such as rebound hammer and ultrasonic pulse velocity, for assessing concrete compressive strength. These methods, though being favorably not invasive and easy to be extended to a larger number of elements, are affected by many contingency factors. The SonReb technique suggests combining the two methods to partially offset their low reliability if considered separately. For years, this concept was introduced in order to improve the evaluation compared with the use of one NDT. In order to combine the ultrasonic pulse velocity and rebound hammer, many empirical, multiparametric models were proposed in the literature as linear, power, exponential, or polynomial. However, the variety of these models emphasizes that they can give a correct strength prediction only for the particular cases that they are derived for. Therefore, to assess concrete on site, the strength should be predicted using a calibration procedure due to the variability of existing concrete mixes. This paper presents a brief outline of the key aspects of strength assessment, including the different approaches used to build the SonReb model and a calibration procedure for assessing concrete strength. A comparison study between the different approaches is proposed, and a performance analysis using Monte Carlo simulations is discussed. Finally, the estimation capacity of the existing model identification approaches is investigated, and the effect of the “trade-off” is analyzed for different random sampling with varying the number of cores.

This paper presents the results of a series of laboratory measurements, carried out to study how the ground-penetrating radar (GPR) signal is affected by moisture variation in wood material. The effects of the wood fibre direction, with respect to the polarisation of the electromagnetic field, are investigated. The relative permittivity of wood and the amplitude of the electric field received by the radar are measured for different humidity levels using the direct-wave method in wide angle radar reflection configuration, in which one GPR antenna is moved while the other is kept in a fixed position. The received signal is recorded for different separations between the transmitting and receiving antennas. Dielectric constants estimated from direct waves are compared to those estimated from reflected waves: direct and reflected waves show different behaviour when the moisture content varies, due to their different propagation paths.

The sustainable development and preservation of natural resources have highlighted the critical need for the effective maintenance of civil engineering infrastructures. Recent advancements in technology and data digitization enable the acquisition of data from sensors on structures like bridges, tunnels, and energy production facilities. This paper explores “smart” uses of these data to optimize maintenance actions through interdisciplinary approaches, integrating artificial intelligence in civil engineering. Corrosion, a key factor affecting infrastructure health, underscores the need for robust predictive maintenance models. Supervised Machine Learning regression methods, particularly Random Forest (RF) and Artificial Neural Networks (ANNs), are investigated for predicting structural properties based on Non-Destructive Testing (NDT) data. The dataset includes various measurements such as ultrasonic, electromagnetic, and electrical on concrete samples. This study compares the performances of RF and ANN in predicting concrete characteristics, like compressive strength, elastic modulus, porosity, density, and saturation rate. The results show that, while both models exhibit strong predictive capabilities, RF generally outperforms ANN in most metrics. Additionally, SHapley Additive exPlanation (SHAP) provides insights into model decisions, ensuring transparency and interpretability. This research emphasizes the potential of integrating Machine Learning with empirical and mechanical methods to enhance infrastructure maintenance, providing a comprehensive framework for future applications.