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This paper discusses the application of machine learning techniques, notably artificial neural networks (ANN), in the fracture analysis of semi-infinite elastic plates with edge cracks. The Stress Intensity Factor (SIF) model for a…
Machine learning has emerged as a powerful tool in various scientific fields for developing data-driven models, reducing the need for extensive physical testing. In this study, the fracture load was first predicted using Theory of Critical…
This paper presents a novel method of assessing structural damage in beams exposed to moving loads via acceleration signals through experimental studies. In this study, beams are supported on both ends, and their dynamic response to moving…
Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided…
This research examines the mechanical characteristics of polymer composites (PMMA) that are reinforced with Hydroxyapatite (HAp), with a particular emphasis on the Elastic Modulus and Compressive Strength. The investigation employs a…
This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough…
Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the…
Variable amplitude loading is frequently applied to welded structures in practical engineering, and fatigue failure is a prevalent problem. In recent years, machine learning is a useful technique for predicting fatigue life. However, it is…
In this paper, a machine learning-based framework is developed to quickly evaluate the structural safety of trusses. Three numerical examples of a 10-bar truss, a 25-bar truss, and a 47-bar truss are used to illustrate the proposed…
The present study develops a knowledge-based machine learning framework for estimating the fatigue life of Al alloy using a combination of data-driven models and an empirical equation. Three different ML models were developed and tested…
This study uses theoretical stress intensity factor data to assess how well machine learning models predict crack length. Thin-walled damage plate under various modes was evaluated based on the theoretical relation. Using theoretical data…
In the past five years, the implementation of machine learning (ML) techniques has surged in civil engineering applications, particularly for optimizing and predicting solutions to various challenges. More robust prediction models may be…
In recent studies, piezoelectric actuators have been recognized as a practical and effective material for repairing cracks in thin-walled structures, such as plates that are adhesively bonded with piezoelectric patches due to their…
The paper describes the implementation of 3D numerical simulation in machine learning models used in infrared thermographic nondestructive testing. The enhancement of generalizability of such models emerges as a decisive factor for…
Reliable discrimination of small damage states under operational variability requires uncertainty aware Structural Health Monitoring. A pipeline is presented that couples guided wave physics with supervised machine learning to classify…
Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing…
Various input variables, including corrosion time, fretting force, stress, lubrication, heat-treating, and nano-particles, were evaluated by modeling of stress-controlled fatigue lifetimes in AlSi12CuNiMg aluminum alloy of the engine…
In the last four decades, bonded composite repair has proven to be an effective method for addressing crack damage propagation. On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical…
In this paper an automatic procedure based on a machine learning approach is proposed to classify ductile cast iron specimens according to the American Society for Testing and Materials guidelines. The mechanical properties of a specimen…
Different structural and machine elements are used over the ages. These are subjected to various loads like static and dynamic load, temperature, corrosion etc. Due to the above-mentioned reasons, ageing of the structural elements occur.…
The dynamic experimental and numerical analysis of cracked beams has been studied with the aim of quantifying the influence of depth crack on the dynamic response of steel beams. Artificial Neural Method ANN has been used where a numerical…
Arch bridges play an important role in rural roads in China. Due to insufficient funds and a lack of management techniques, many rural arch bridges are in a state of disrepair, unable to meet the increasing transportation needs. Thus, it…
The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data…
Machining with a cutting edge with extensive damage or a fractured cutting edge significantly influences the machining performance. Therefore, investigations on tool wear behavior, their forms, and wear mechanisms will be very helpful in…
