Artificial Neural Network Model for Low Strength RC Beam Shear Capacity

This research was to investigate how the shear strength prediction of low strength reinforced concrete beams will improve under an ANN model. An existing database of 310 reinforced concrete beams without web reinforcement was divided into three sets of training, validation and testing. A total of 224 different architectural networks were tried, considering networks with one hidden layer as well as two hidden layers. Error measures of strength ratios were used to select the best ANN model which was then compared with 3 conventional design code equations in predicting the shear strength of 26 low strength RC beams. Even though the ANN was the most accurate, it was less conservative compared with the design code equations. A model reduction factor based on the characteristic strength concept is derived in this research and used to modify the ANN output. The modified ANN model is conservative in terms of safety and economy but not overly conservative as the conventional design equations. The procedure has been automated such that when new experimental sets are added to the database, the model can be updated and a new model could be developed

Analysis of stiffness and flexural strength of a reinforced concrete beam using an invented reinforcement system

In this study, we conducted experimental tests on two specimens of reinforced concrete beams using a three-point bending test to optimize the flexure and stiffness designs. The first specimen is a reinforced concrete beam with an ordinary reinforcement, and the second specimen has an invented reinforcement system that consists of an ordinary reinforcement in addition to three additional bracings using steel bars and steel plates. The results of the flexure test were collected and analyzed, and the flexural strength, the rate of damage during bending, and the stiffness were determined. Finite element modeling was applied for both specimens using the LSDYNA program, and the simulation results of the flexure test for the same outputs were determined. The results of the experimental tests showed that the flexural strength of the invented reinforcement system was significantly enhanced by 15.5% compared to the ordinary system. Moreover, the flexural cracks decreased to a significant extent, manifesting extremely small and narrow cracks in the flexure spread along the bottom face of the concrete. In addition, the maximum deflection for the invented reinforced concrete beam decreased to 1/3 compared to that of an ordinary reinforced concrete beam. The results were verified through numerical simulations, which demonstrated excellent similarities between the flexural failure and the stiffness of the beam. The invented reinforcement system exhibited a high capability in boosting the flexure design and stiffness

Evaluation of deep learning models for classification of asphalt pavement distresses

Transfer learning (TL) offers a convenient methodology for exploiting the capability of deep convolutional neural networks (DCNNs) for many image classification tasks including the classification of pavement distresses. Seven state-of-the-art DCNNs were retrained to classify asphalt pavement distresses grouped into eight classes using TL techniques. The aim was to evaluate the predictive performances of the selected DCNNs in order to provide some guidelines on selection of DCNNs for pavement application. The results show some existing DCNN’s are better than others for developing pavement distress classification models using the specific TL approach adopted in the study. The predictive ability of each model varied depending on distress class as some models with very low overall accuracy showed excellent results for individual distress class(s). Based on a combination of various performance metrics including F1-score, area under ROC curve, optimal operating threshold, training time, and model size, the best performing network had a relative score that was found to be significantly higher than the next two top-performing models. The best-performing networks were characterised by lower proportions of false negative values, low ambiguity scores, and well-defined t-SNE clusters that showed clear separation between the eight different pavement distress classes considered