Fiber-reinforced polymer (FRP) is an innovative material in the construction industry. It is beneficial because of its toughness, and unlike steel, it is not prone to corrosion. Some research studies focus its behavior as a reinforcement in concrete while deriving several equations pertaining to its shear strength capacity. This study used the artificial neural network modeling technique to derive a more accurate solution to predict concrete shear capacity with FRP as reinforcement. Experimental data from previous studies were collected and used to train the model. The parameters considered were compressive strength of concrete, FRP ratio, beam dimensions, and modulus of elasticity. As a result, the model consistently provides a better prediction of the shear capacity of concrete against existing models like ACI 440.1R-03, ACI 440.1R-06, and El-Sayed. Furthermore, the ANN model showed no sign of disarray in predicting every parameter compared to other existing models. According to ACI 440.1R-06, FRP bars largely affect the total shear capacity of concrete. In the model provided by ACI, FRP reinforcement’s axial stiffness accounts linearly to the shear strength capacity of concrete. Since then, the predicted capacity in accordance with the ACI was excessively conservative. With respect to the derived model, axial stiffness offered a variation in the shear capacity. The proposed ANN model can be utilized for the design since the minimum ratio between the actual test result yields to 0.77 which is greater than the strength reduction factor of 0.75. Parametric studies were also conducted to show the effect of the modulus of elasticity of FRP, FRP ratio, and beam dimensions on the shear capacity.

Riverbank collapse was recorded in the Philippines especially during typhoons and heavy rains. To analyze the possibility of failure, Slope/W under the Geostudio package was used as a finite element modeling technique in the study. The simulation determined the effect of the factor of safety (FOS) from the six (6) different assumed slopes and 15 different soil properties. The assumption of the present paper is that the properties of the soil and slope of the riverbank can determine the stability status of the sloping riverbank. There was a total of 90 simulations that were analyzed using the Morgenstern-Price method. The simulation results concluded that the highest and safe values of the factor of safety were recorded in simulations 1, 5 and 12 which are all more than 1.50. The simulations with a lower value of cohesion resulted in a lower value of the factor of safety. The correlation assessment result revealed that the slope instability or failure of slopes of riverbanks can be attributed to the cohesion values and the angle of internal friction (ϕ). It is recommended using a flatter slope and high values of cohesion which can be attained by riverbank improvement. If lower values of cohesion were detected, additional measures like bank stability should be proposed. The measures to be implemented should improve the FOS value which would improve the stability of slopes, thus preventing the vulnerability to riverbank collapse.

Carbonation is a dangerous threat to concrete since it reduces the alkalinity of normal or self-compacting concrete (SCC), allowing iron to corrode and spall the cover. The goal of this research is to use an artificial neural network to create a corrosion prediction model for steel in self-compacting concrete that has been subjected to carbonation. In this study, MATLABR2019a was used to create a feedforward back propagation neural network. As a training function, the researchers utilized the Levenberg-Marquardt back propagation (TRAINLM) which adjusts weights and bias values using Levenberg-Marquardt optimization. The researchers used gradient descent with momentum weight/bias learning (LEARNGDM) for the adaptation learning function, which is a technique that aids the gradient in determining which way to go. The network’s performance was measured using the mean square error (MSE). The Hyperbolic tangent sigmoid transfer function (TANSIG) was also employed as the transfer function since the values obtained by this function range from +1 to -1, considering both the positive and negative aspects of the parameter. To minimize overfitting, the number of hidden nodes should be fewer than the number of input parameters. The researchers tested 4-12 hidden nodes. Modeling was done using data from 102 experimental studies of self-compacting concrete exposed to corrosion. Using feed-forward back propagation ANN with 1 hidden layer and 8 hidden nodes, a Pearson R-value of 0.98748 and a mean square error of 0.5725 were obtained. The factor that most affect the carbonation depth were water-cement ratio and fly ash content. The suggested model was able to analytically describe the connection and behaviors of the various mixtures to the carbonation depth in the parametric investigation. The parameters characteristics were likewise described by the model.

The paper aims to examine the factors that affects the successful implementation of the Mastery-based Individualized Learning Enhancement System (MILES) in the Far Eastern University (FEU) Institute of Technology. Two periods were analyzed which are the initial implementation, and this is the start of the pandemic period, and the year after the initial implementation of MILES. The Artificial Neural Network (ANN)-Particle Swarm Optimization (PSO)-based Sensitivity Analysis (SA) was utilized to determine the relative importance (RI) index among the influencing factors that affects the students’ level of satisfaction of the MILES implementation. Survey questionnaires were deployed through the canvas platforms and were answered by the students. In the initial survey, a total of 5763 students responded. For the SY 2020-2021, it was observed that the most influential variable to the student’s performance during the MILES Implementation is Course Adviser Rating while the parameters with the least impact to the student’s performance is the Student’s Status as regular or irregular student. For the survey on SY 2021-2022, the highest relative index is for the lesson preference while the lowest importance index is for the opportunities. Findings of the study shows that the use of NN-PSO based sensitivity analysis is an effective tool for establishing the significance of each variable to a target output.