Nondestructive testing (NDT) that uses ultrasonic waves is a reliable experimental technique for characterisation of materials in existing concrete structures and for safety evaluations of these structures. The wave velocity is related to the stiffness of material and the wave attenuation can be used to evaluate damaged concrete structures. This paper proposes an experimental technique to quantitatively measure the wave attenuation using contact ultrasonic transducer for NDT. The proposed technique is subject to interference as a result of the transducer-coupling condition. Unbiased attenuation can be obtained by self-compensating the signals measured in a lead–zirconate–titanate ceramic material inserted between a conventional transducer and a sample. The reproducibility and relevancy of the proposed technique are demonstrated using examples of the technique applied to cement paste and concrete. Also, the relevancy of the attenuation measurements for damage evaluation of cement-based material is demonstrated based on a theoretical attenuation model.

In this paper, the plastic settlement of mortar is analyzed on the basis of small strain consolidation theory and the approach is compared with experimental data. The amount of settlement caused by the self-weight of bulk mortar was measured using a noncontact laser measurement device. In addition, tests were performed on systems containing different geometries of embedded reinforcement to measure the effect of specimen and inclusion geometry on settlement. The influence of mixture proportions and cover depth were analyzed. The experimental results were correlated with results from the numerical simulation of fresh mortars using consolidation analysis. The results suggest that consolidation theory can be used as a reasonable approach to analyze plastic settlement. Further, this model can be used to demonstrate the effects of differential settlement over reinforcing bars or changes in section height.

Nondestructive evaluation using the propagation of an impact-induced surface wave can be effectively applied in estimating in situ material properties. In this study, to evaluate information of a surface waveform beyond the simple wave velocity, artificial intelligence engines are employed to estimate simulation parameters, that is, the properties of elastic materials. The developed artificial neural networks are trained with a numerical database having secured its stability. In the process, the appropriate shape of the force–time function for an impact load is assumed so as to avoid Gibbs phenomenon, and the proposed principal wavelet-component analysis accomplishes a feature extraction with a wavelet transformed signal. The results of estimation are validated with experiments focused on concrete materials.