Carbonation depth involves complex physical process and interactions of multiple variables and is thus extremely complicated to predict in concrete structures. It is imperative to quantify this depth due to its vital role in the corrosion of rebars in recycled aggregate concrete (RAC). This paper developed a novel carbonation depth prediction model from a large database of 445 experimental results using artificial neural network (ANN). The relative importance and effect of the independent parameters in the carbonation depth are identified using Garson index and parametric analysis, respectively. Among all the architectures considered, the N 8-10-1 having 10 nodes in the hidden layer provided the best prediction in good agreement with experimental results. The model demonstrated superior performance relative to existing carbonation depth equations in the literature. Despite the presence of fuzziness in the data, the effect of each variable in the development of carbonation is explored in great detail. The model proposed here can provide a robust prediction of carbonation depth that can be used as a basis for assessing the structural health of recycled aggregate concrete structures.

Interest in FRP composite bars as reinforcement to concrete has increased over the years as it showed solutions to the drawbacks of steel such as its corrosion issues and vulnerability when employed in adverse environmental conditions. However, it is still not widely incorporated as a replacement to conventional steel primarily due to the complexity of its bond strength mechanism. This, therefore, imposes the need to establish a comprehensive relationship for the bond property of the FRP reinforced concrete. This paper developed a novel Artificial Neural Network (ANN) bond strength prediction model for FRP reinforced concrete using 184 hinged beam database from various existing experiments. From series of simulations performed, the model N 7-10-1 with ten nodes in the hidden layer appeared to be the best fit with the experimental results yielded the most favorable performance among other existing models. From the parametric analysis conducted, the compressive strength of the FRP reinforced concrete has proved to be the most dominant parameter in evaluating its bond behavior as determined by relative importance of 17.82%. Overall, the proposed ANN model has demonstrated the best prediction for FRP bond strength in comparison to previous studies and code equations.

Ultra-High Strength Concrete (UHSC) is known for its brittleness compared to traditional concrete, which can lead to sudden collapses. When it comes to columns, failures are particularly serious and require the use of confinement models to accurately predict the strength and strain of confined UHSC columns. While previous confinement models exist, many equations either underestimate or overestimate the confinement of concrete due to idealized assumptions and the exclusion of significant variables. This study employs a hybrid machine learning approach to capture the complex interactions in confinement behavior and accommodate a broader range of peak strength and axial strain parameters in UHSC. Statistical performance measures indicate the superiority of the proposed models over existing equations. Through causal inference, the study assesses the effects and relative importance of each parameter on peak strength and axial strain. The visualizations provided by the performance plots helped identify patterns and correlations that would have been difficult to discern through numerical analysis alone. The developed NN-PSO models are proven effective in reasonably predicting the peak strength and axial strain of UHSC columns.

Structures built on high seismic areas are likely to experience earthquake liquefaction. This in turn will compromise the integrity of the structures and thus, assessment of the susceptibility to liquefaction is essential. To evaluate the likelihood and severity of earthquake induced liquefaction particularly in the 2nd district of Laguna, 74 geotechnical reports from various locations were collected. Using deterministic approach, safety factors and liquefaction severity index were calculated at different locations to generate liquefaction probability and severity maps. Results showed that there is a wide range of liquefaction severity levels from very low severity of 3.8% of the areas to high severity of 5.06% of the areas. The probability map further showed that an average of 90.49% of the areas are susceptible to liquefaction when an 8.0 earthquake magnitude occurs. The developed maps can be used by site planners and engineers to identify the severity of liquefaction at specific locations and appropriately apply remedial measures in the design of structures.

Corrosion of steel reinforcement due to hostile environments is regarded as one vital structural health concerns in concrete structures. Specifically, the development of corrosion affects the necessary bond strength of rebar in concrete contributing to the loss of resilience and possible structural failures. It is thus essential to understand the effects of corrosion on bond strength so that remedial measures can be done on existing and deteriorating RC structures. Hence, this study investigated through laboratory experiments and Artificial Neural Network (ANN) modeling the effects of corrosion on bond strength. Experimental results showed that at small amounts of corrosion less than 0.27%, the bond strength was observed to increase. At these levels, the amounts of corrosion products were sufficient enough to expand freely through the permeable structure of concrete and occupy the pore spaces. Beyond this level, however, the bond strength of concrete deteriorated significantly. There was an observed average decrease of 1.391 MPa in the bond strength values for every percent increase in the amount of corrosion. The expansive and progressive internal radial stress due to corrosion resulted to the development of internal and surface cracks in concrete. In the parametric investigation of the derived ANN model, the bond strength was also observed to decline continuously with the growth of corrosion derivatives as represented by the relative magnitudes of the ultrasonic pulse velocity (UPV). The prediction results of the model can be utilized as basis for design and select appropriate mitigating measures to prolong the service life of concrete structures.