This paper describes the application of two artificial intelligence‐ (AI‐) based methods to predict the 28‐day compressive strength of fiber‐reinforced high‐strength self‐compacting concrete (FRHSSCC) from its ingredients. A series of 131 data samples collected from various published literature sources were used for training, validation, and testing models. Various AI models were developed with different training algorithms and a number of nodes in the hidden layer to obtain the optimal model for the FRHSSCC data. It is shown that the performances of the artificial neural network (ANN) were better than that of the adaptive neurofuzzy inference system (ANFIS) model. Specifically, the overall coefficient of determination (R²) of the ANN and ANFIS models was 0.9742 and 0.9584, respectively. The sensitivity analysis was also conducted with the ANN model to investigate the effects of input parameters on the output. The results from the sensitivity analysis revealed that the compressive strength of FRHSSCC at 28 days was more sensitive with the changes of water by cement ratio (WCR) parameter and insensitive with varying amounts of fiber (VOF). Finally, it can be concluded that the application of artificial intelligence shows the great potential in the prediction of compressive strength of FRHSSCC.

Since 1987 when dry-point-contact (DPC) transducers were invented in the USSR, ultrasonic shear wave devices based on those transducers have been commercialized and have become one of the most effective technologies for imaging concrete. That said, the objectives of this paper are (1) to provide a brief review of the historical development of these powerful devices and (2) to provide a comprehensive assessment of their capabilities in imaging internal entities and structural defects. Regarding the former, the paper presents the context that gave birth to DPC technology and different generations of ultrasonic shear wave devices for concrete inspection. For the latter, one of the state-of-the-art ultrasonic shear wave devices (MIRA 3D) was used to collect data on concrete specimens with different built-in flaws/defects. Those data are then visualized with a commonly used data processing algorithm, the so-called synthetic aperture focusing technique (SAFT). Finally, based on the resulting images, the capabilities of the device are discussed in detail for each concrete imaging problem. A main limitation of ultrasonic shear wave technique for concrete inspection is that it requires a significant amount of time and effort for data collection.