[HTML][HTML] Application of meta-heuristic algorithms for training neural networks and deep learning architectures: A comprehensive review

M Kaveh, MS Mesgari - Neural Processing Letters, 2023 - Springer
The learning process and hyper-parameter optimization of artificial neural networks (ANNs)
and deep learning (DL) architectures is considered one of the most challenging machine …

Vision-based concrete crack detection using a hybrid framework considering noise effect

Y Yu, B Samali, M Rashidi, M Mohammadi… - Journal of Building …, 2022 - Elsevier
Diagnosing surface cracks of concrete structures has been a critical aspect of assessing
structural integrity. Existing diagnosis technologies are time-consuming, subjective, and …

Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network

Y Yu, S Liang, B Samali, TN Nguyen, C Zhai, J Li… - Engineering …, 2022 - Elsevier
This study presents the application of deep learning technology in torsional capacity
evaluation of reinforced concrete (RC) beams. A data-driven model based on 2D …

Research overview on edge detection algorithms based on deep learning and image fusion

B Tian, W Wei - Security and Communication Networks, 2022 - Wiley Online Library
Edge detection is a boundary‐based segmentation method to extract important information
from an image, and it is a research hotspot in the fields of computer vision and image …

Corrosion and coating defect assessment of coal handling and preparation plants (CHPP) using an ensemble of deep convolutional neural networks and decision …

Y Yu, AN Hoshyar, B Samali, G Zhang… - Neural Computing and …, 2023 - Springer
In view of the problems of ineffective feature extraction and low detection accuracy in
existing detection system, this study presents a novel machine vision-based approach …

[HTML][HTML] Integration of TLS-derived Bridge Information Modeling (BrIM) with a Decision Support System (DSS) for digital twinning and asset management of bridge …

M Mohammadi, M Rashidi, Y Yu, B Samali - Computers in Industry, 2023 - Elsevier
In the current modern era of information and technology, the concept of Building Information
Modeling (BIM), has made revolutionary changes in different aspects of engineering design …

Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring

A Waqas, D Kang, YJ Cha - Structural Health Monitoring, 2024 - journals.sagepub.com
This paper proposes a framework for obstacle-avoiding autonomous unmanned aerial
vehicle (UAV) systems with a new obstacle avoidance method (OAM) and localization …

Deep learning of electromechanical impedance for concrete structural damage identification using 1-D convolutional neural networks

D Ai, F Mo, J Cheng, L Du - Construction and Building Materials, 2023 - Elsevier
Common damages in concrete materials and structures are usually in small sizes at initial
stage, which induce small stiffness and mass loss being difficult to evaluate severity level …

[HTML][HTML] Hybrid SFNet model for bone fracture detection and classification using ML/DL

DP Yadav, A Sharma, S Athithan, A Bhola, B Sharma… - Sensors, 2022 - mdpi.com
An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-
consuming process. The development of machine learning (ML), as well as deep learning …

Automated identification of compressive stress and damage in concrete specimen using convolutional neural network learned electromechanical admittance

D Ai, F Mo, Y Han, J Wen - Engineering Structures, 2022 - Elsevier
For the first time, this paper proposed a simple two-dimensional convolutional neural
network (2-D CNN) integrated with electromechanical admittance (EMA, inverse of …