Carbonation is a deleterious concrete durability problem which may alter concrete microstructure and yield initiation of corrosion in reinforcing steel bars. Previous studies focused on the use of Artificial Neural Networks (ANN) for the prediction of concrete carbonation depth and to minimize the need for destructive and elaborated civil engineering laboratory tests. This study aims to provide improved accuracy of simulation and prediction of carbonation with an ANN architecture including eighteen input parameters employing alternative Scaled Conjugate Gradient (SCG) function. After ensuring a promising value of the coefficient of correlation as high as 0.98, the influence of proposed input parameters on the progress of carbonation depth was studied. The results of this parametric analysis were observed to successfully comply with the conventional civil engineering experience. Hence, the employed ANN model can be used as an efficient tool to study in detail and to provide insights into the carbonation problem in concrete.

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The complex shear modulus (G*) and phase angle (δ) are fundamental viscoelastic rheological properties used in the estimation of rutting and fatigue pavement distress in asphalt binder. In the tropical regions, rutting and fatigue cracking are major pavement distress affecting the serviceability of road infrastructure. Laboratory testing of the complex shear modulus and phase angle requires expensive and advanced equipment that is not obtainable in major laboratories within the developing countries of the region, giving rise to the need for an accurate predictive model to support quality pavement design. This research aims at developing a predictive model for the estimation of rutting and fatigue susceptive of asphalt binder at intermediate and high pavement temperatures. Asphalt rheological and ageing test was conducted on eight mixes of modified binders used to build the study database containing 1976 and 1668 data points for rutting and fatigue parameters respectively. The database was divided into training and simulation dataset. The Gaussian process regression (GPR) algorithm was used to predict the rutting and fatigue parameters using unaged and aged conditioned inputs. The proposed GPR was compared with the support vector machine (SVM), recurrent neural networks (RNN) and artificial neural network (ANN) models. Results show that the model performed better in the estimation of rutting parameter than the fatigue parameter. Further, unaged input variables show better reliability in the prediction of fatigue parameter.

Structural health management constitutes an essential factor in ensuring the durability of concrete structures. Cracks in concrete, reinforcement corrosion, alkali-silica reaction, and efflorescence attacks are commonly concrete defects that can be identified visually. However, detection and classification of these defects in concrete bridges and other high-rise concrete structures are difficult and expensive process in manual approaches. In this research, a deep learning application is applied to detect and classify concrete defects. Concrete images from the public repository were used to create the explored database. The database was divided into training and validation subsets. The visual geometry group (Vgg19), neural search architecture (nasnetlarge), and residual inception block (vinceptionresnetv2) algorithms were used in analysing the images.  The results of the overall performance show that Vgg19 algorithm recorded higher accuracy in the detection and classification of concrete defects as compared to nasnetlarge and inceptionresnetv2 algorithms. The efficiency of the proposed approach was evaluated using a new dataset containing images of concrete defects. The outcome of this research strongly shows that deep learning models could enhance the efficiency of concrete structural health monitoring in a multi-classification scenario.

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