This paper presents the development of a data-driven deep neural network (DNN) for evaluating the fire resistance time of fiber-reinforced polymer (FRP) strengthened concrete beams. The model was trained for a scaled and unscaled dataset. For this, a comprehensive dataset of FRP-strengthened concrete beams with different geometry, insulation configuration, applied loading, and material characteristics was compiled. The DNN structure was selected after an extensive hyperparameter tuning in conjunction with ten-fold cross-validation scheme. The effect of different input parameters on the fire resistance prediction was analyzed. The DNN model developed using scaled data provides a reasonably accurate estimate, of the fire resistance of FRP-strengthened concrete beams with an R2 value of almost 92%. The developed model is further utilized to evaluate the impact of different parameters on fire resistance prediction for FRP-strengthened concrete beams. Results from the analysis indicate the thermal properties of insulation play an important role in determining the fire resistance of FRP-strengthened concrete beams.

This research employs a novel Bayesian estimation technique to perform model updating on a coupled vehicle-bridge finite element model (FEM) for the purposes of classifying damage on a reinforced concrete bridge. Unlike existing Artificial intelligence (AI) techniques, the proposed methodology makes use of an embedded FEM, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM, and the results are compared against results obtained for “true” parameter values. Furthermore, a sensitivity study is conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when using experimental data to update vehicle-bridge FEMs with the Bayesian estimation technique.

Artificial Intelligence has one of the most efficient methods for solving engineering and materials problems because of its impressive performance and can reach higher accuracy. The Deep Learning theory is an approach based on Deep Neural Networks for establishing numerical analysis and value predictions. This paper proposes a fresh approach, using a Deep Learning model for predicting the compressive strength in a particular concrete just based on non-destructive test measurements (NDTs). The model proposed is an attractive alternative to estimate the resistance of compressive strength in any structure, just taking data like ultrasonic pulse velocity, electrical resistivity, and resonance frequencies. The present work employs data science techniques to find the correlation values between the NDTs and the compressive strength effort and realized broad numerical exploration about concrete performance. An amount of 285 specimens of concrete were monitored during this research. The model proposed contains 600 neurons and uses a Rectified Linear Unit and Sigmoid as activation functions where the NDTs were established as the input data. The dataset was segmented into two groups: train and test. In order to evaluate the model, the authors tested it in a validation set with different concrete features, achieving an accuracy of 94%.

This paper presents and explains an implementation of artificial intelligence for the real-time crack detection of ultra-high-performance concrete (UHPC). A deep learning algorithm is employed to process image data and to identify physical cracks. The state-of-the-art object detection method generates accurate results with small datasets. To provide training and validation images, UHPC specimens are cast with various fibers and loaded per an ASTM standard, including steel and synthetic (collated and monofilament polypropylene) fibers. After testing, sample images are labeled with an annotation tool and the algorithm is trained and validated with an image recognition approach, leading to a mean average precision (mAP) of 99%. The occurrence of cracking and propagation are linked with the applied load level to appraise the influence of the mixed fibers in the crack development of UHPC. It needs to be noted that the adopted deep learning architecture is incapable of quantifying crack width and area directly; therefore, a Java-based image processing program is used to measure these properties of the specimens. The characteristics of the load-induced cracks are dominated by the fiber types. Plain UHPC fails rapidly and the flexural capacity of UHPC increases with the presence of the fibers; especially, the UHPC with steel fibers demonstrates higher flexural capacities than other cases.

In this paper, the artificial neural network (ANN) method is utilized to predict ground-penetrating radar reflection amplitudes from four different inputs, namely, temperature, ambient relative humidity, chloride level, and corrosion condition on the surface of the reinforcing bar. A total of 288 ground penetrating radar (GPR) data points were collected from a series of chloride-contaminated concrete slabs under various environmental profiles that were used to train, validate, and test the proposed ANN model. The ANN model performed well in predicting the GPR reflection signals, with the overall coefficient of determination (R2) being 0.9958. The overall mean squared error (MSE) and root mean squared error (RSME) values are 0.015 and 0.122, respectively. These values are very low, which means that the ANN model has an excellent prediction capability. The research results show that the GPR reflection amplitudes are more sensitive to the temperature changes and chloride level parameters than the ambient relative humidity and rust condition on the reinforcing bar surface. Using the ANN method to predict the GPR reflection amplitudes is relatively new for structural concrete applications. This study paves the way for further developments of neural networks in civil and structural engineering.