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.

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.

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.

Application of corrosion inhibitor has been practiced to counter the development of corrosion in reinforced concrete. However, utilization of corrosion inhibitor may compromise the bond between concrete and steel bar. The study investigated the bond performance of corroded rebar in concrete with corrosion inhibitor using impressed current technique. The relationship of bond strength and corrosion accumulated by reinforcement, non-coated (NC) and coated (C) with corrosion inhibitor are the main variables of this study. Thirty samples were subjected to impressed current technique to accelerate corrosion. For every 7-day interval within a 28-day period, 6 samples (NC and C) were tested using single pull-out test to determine the bond strength. Upon using ANOVA, results demonstrated an increasing trend of corrosion level for samples exposed longer to impressed current, with a P-value of 5.11 e-07. On the other hand, a SLRA derived model for bond strength as a function of corrosion level showed that bond strength of reinforcement (C and NC) decreases by 0.5508 MPa, and 1.2078 MPa respectively. Both models are deemed to be significant having a P-value and Multiple R of 0.0371, 0.5415 and 0.0009, 0.7667, for C and NC respectively.

The West Valley Fault Line stretches across Metro Manila and School buildings are essential facilities as it serves as an evacuation center for such events. Rapid Visual Survey is a method in screening structures for potential earthquake hazards. This method was employed to assess seismic hazards of the public schools in Metro Manila within 5km from the fault line. Data collection form from the FEMA P-154 which includes building identification information regarding the target schools was used. Age of the building, irregularities, and soil type were determined on site and during the planning stage. A corresponding score was derived and used as level indicator of the potential seismic hazard of the building. It was found out that 14 buildings out of 139 were potentially seismically hazardous and requires further seismic assessment by the LGUs. A hazard map created using ARCGIS showed effectively the distribution and potential grade of damage of every building in each city. The map can be used to raise social awareness and baseline information to promote safety of the communities in the study area.

The composite action of reinforcement in the surrounding concrete involve a complex and non-linear mechanism.Inadequate understanding of the underlying interactions may lead to designs with insufficient amount of bond resistance of reinforcing bars in concrete structures.To investigate the effects of various parameters on the bond strength of steel bars in concrete, 54 cube samples with varying embedded reinforcements and strengths were prepared. The samples were cured for 28 days and tested using ultrasonic pulse velocity (UPV) test for sample homogeneity and single pull out test for bond strength.Data gathered in the experiment were used in the development of bond strength model as a function of compressive strength, concrete cover to rebar diameter ratio, embedment length, and UPV using artificial neural network (ANN). Of all the bond strength models considered from various literatures, the neural network model provided the most satisfactory prediction results in good agreement with the bond strength values obtained from the experiment. The UPV parameter was found to be one of the most significant predictors in the neural network model having a relative importance of 20.57%. This suggest that the robust prediction performance of the bond model was attributed to this essential component of the model. The proposed model of this study can be used as baseline information and rapid non-destructive assessment for zone wise strengthening in reinforced concrete.

One of the most significant innovations on the workability of concrete that was achieved in recent years is self-compacting concrete (SCC). This desirable performance can be attained through the addition of admixtures to enhance its properties. In this study, superplasticizers were blended with fly ash and air entraining admixtures and were tested for Slump Flow, V-Funnel, L-Box, U-Box, and Screen Stability tests based on the European Federation of National Associations Representing for Concrete (EFNARC) specifications and guidelines for SSC. Based on the results of the study, Fly ash with spherical smooth texture enhances the lubrication between the concrete particles while the air-entrainer provides microscopic bubbles acting as ball bearings between aggregates. The best result was obtained in the specimens containing 5.0% superplasticizers due to its dispersibility effect and reduced flow resistance. In general, the air entraining agent blended with 3.7% superplasticizer exhibited the best performance in all workability test conducted.

An essential component in decision making for site planners is the availability of risk maps to various geological hazards. Liquefaction in particular can be devastating and impose disastrous damage to existing structures built in earthquake prone areas like the province of Mindoro. Through the aid of in situ data, a simplified method of evaluating earthquake induced liquefaction potential was carried out in this study. This is to address the difficulty and high cost necessary to carry out the development of a liquefaction risk map. Borehole data were collected from different locations in Mindoro and the earthquake liquefaction severity index in each location were calculated using deterministic approach. Results showed that different levels of liquefaction severity were obtained in various areas of Mindoro. There were locations exhibiting manifestations of surface liquefaction due to 7.1 Mw earthquake with a peak ground acceleration of 0.4g. The generated liquefaction severity maps can be utilized as baseline information in selecting appropriate geotechnical intervention for soil improvement and stabilization. Further, the indices can be used as additional dimension of evaluating the holistic reliability of existing engineering structures.