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 production of fiber-reinforced concrete presents a complex reaction system, posing significant challenges in determining appropriate material proportions to achieve targeted mechanical properties. To address this issue, this study proposes novel Artificial Neural Network (ANN) models for predicting the compressive and flexural strengths of fiber-reinforced concrete using a backpropagation feed-forward algorithm. A wide range of concrete mix designs was prepared and tested using cylindrical samples for compressive strength and beam samples for flexural strength. Polypropylene fibers were incorporated into the mixes, and all specimens were cured for 28 days in a water-saturated lime solution. The results demonstrated that the ANN models produced strength predictions that closely aligned with experimental data, yielding high correlation values of 99.46% and 98.57% for compressive and flexural strengths, respectively. The best-fit models exhibited mean squared errors of 0.0024 (compressive) and 0.44 (flexural). Furthermore, parametric analysis indicated that the proposed models effectively captured the constitutive relationships among the concrete components and successfully represented the dominant mechanical behavior of the tested specimens.

Predicting the compressive strength of self-compacting concrete (SCC) is one of the complicated tasks because of its complex behavior due to the reaction of chemical and mineral content and the hydration process of cement. The distinct difference of SCC to normal concrete is its improved workability or also known as rheology that is divided into four categories namely viscosity, flow ability, passing ability, and resistance to segregation. It was proposed in this study to include the rheological behavior of SCC to the prediction of its compressive strength. Neural network was utilized for predicting the 28th day compressive strength of SCC. A 97.78% prediction rate was achieved using a feed-forward back-propagation ANN with 1 hidden layer and 8 hidden nodes. Tansig transfer function was used as activation function. The model has a Pearson R value of 0.991 and mean square error (MSE) of 3.42.