Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks

no code implementations • 22 Dec 2023 • Sarmed Wahab, Mohamed Suleiman, Faisal Shabbir, Nasim Shakouri Mahmoudabadi, Sarmad Waqas, Nouman Herl, Afaq Ahmad

The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with maximum accuracy of 99. 13% and GWO predicted the results with an accuracy of 98. 17%.

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab-Column Connections

no code implementations • 29 Sep 2023 • Sarmed Wahab, Nasim Shakouri Mahmoudabadi, Sarmad Waqas, Nouman Herl, Muhammad Iqbal, Khurshid Alam, Afaq Ahmad

The study is complemented with FEA of slab for validating the experimental results and machine learning predictions. In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data.

Classification of Potholes Based on Surface Area Using Pre-Trained Models of Convolutional Neural Network

no code implementations • 29 Sep 2023 • Chauhdary Fazeel Ahmad, Abdullah Cheema, Waqas Qayyum, Rana Ehtisham, Muhammad Haroon Yousaf, Junaid Mir, Nasim Shakouri Mahmoudabadi, Afaq Ahmad

The classification of images taken at the height of 2 feet has an accuracy value of 87. 33%, 88. 67%, and 92% for classifying the large, small, and normal pavement, respectively.