Published Volumes & Issues
Browse all published issues of International Journal of Technical Innovation in Modern Engineering & Science. Click any issue card to view its papers inline.
Vol.11, Issue 4 · (October - December) 2025 · ID: IJT-2025-10851
Deep Learning-Based Enhancement of ANN Models for Automatic Berthing Systems
Daesoo Lee , Seung-Jae Lee, Yu-Jeong Seo · Division of Naval Architecture and Ocean Systems Engineering, Korea Maritime and Ocean University, Busan, Korea, Korea
🕔 Received 31 May 2025; received in revised form 21 Sep. 2025; accepted 16 October 2025
Keywords: automatic ship berthing, artificial neural network, deep neural network, batch normalization
Vol.11, Issue 4 · (October - December) 2025 · ID: IJT-2025-30926
Experimental Study on the Shear Capacity of RC Deep Beams Strengthened with External V-Shaped Rods
Pramod Rai , Kitjapat Phuvoravan · Department of Civil Engineering, Kasetsart University, Jatujak, Bangkok, Thailand, Thailand
🕔 Received 30 April 2025; received in revised form 29 October2025; accepted 07 November 2025
Keywords: external posttension technique, nonlinear finite element, experimental investigation, shear strengthening, reinforced concrete deep beam, strut-and-tie model
Vol.11, Issue 4 · (October - December) 2025 · ID: IJT-2025-65392
ANN-Based Prediction of Pressure Gradient in Multiphase Two- and Three-Phase Vertical Pipe Flows
Joseph Xavier Francisco Ribeiro , Ruiquan Liao, Aliyu Musa Aliyu, Zilong Liu · Petroleum Engineering College, Yangtze University, Wuhan Hubei, China, China
🕔 Received 14 June 2025; received in revised form 3 December 2025; accepted 13 December 2025
41.58 m/s respectively. Slug flow was the main flow pattern observed. In addition, the transition from churn to annular flow and annular were also observed. Due to the homogeneous nature of the oil-water-air mixture, the three-phase flow was evaluated as a pseudo-two-phase mixture. An Artificial Neural Network (ANN) model developed for the prediction of two-phase and three-phase pressure drop performed better than all models considered during the evaluation. Generally, it was found that the accuracies for pressure drop were considered adequate.
Keywords: artificial neural networks, two-phase flow, three-phase flow, pressure drop, vertical pipes
