使用回归和机器学习模型识别悬臂梁中的损伤

IF 1.7 4区 工程技术 Q3 ENGINEERING, CIVIL
Vikas Khalkar, Arul Marcel Moshi Antony Joseph Decruz, Logesh Kamaraj, Hariharasakthisudhan Ponnarengan, Renjin J. Bright
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引用次数: 0

摘要

制造故障会导致结构出现裂缝。由于裂缝可能导致灾难性的系统故障,因此识别和分类在科学研究中至关重要。结构适配性跟踪的目的是诊断和预测结构适配性。基于自由振动的完整裂缝检测方法被广泛用于发现系统中的潜在裂缝。然而,静态挠度方法仅限于预测裂缝参数。因此,本文采用静态挠度法来确定悬臂梁的裂缝位置和深度。在梁的自由端安装了一个自重,并使用了两个刻度盘量规。梁的自由端安装了一个量规,用于测量自由端挠度。另一个千分表也安装在裂缝附近,用于测量裂缝的静态挠度。对 48 个裂缝试样进行了数值和实验分析,以测量两点的静态挠度。建立了一个回归模型来计算裂缝参数,即梁中的裂缝位置和裂缝深度。为了评估所开发回归模型的可靠性,使用了机器学习模型,即人工神经网络(ANN)和随机森林(RF)进行预测。使用数值和实验数据集开发了回归、ANN 和 RF 模型。回归模型和机器学习模型得出的裂纹深度和位置结果与实际结果一致。使用静态两点挠度作为输入对裂缝参数进行了预测,结果令人鼓舞。因此,静态两点挠度方法可广泛用于检测更复杂结构的未来裂缝。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Damage Identification in a Cantilever Beam Using Regression and Machine Learning Models

Damage Identification in a Cantilever Beam Using Regression and Machine Learning Models

A manufacturing fault causes a defect consisting of a crack in the structure. Identification and classification are essential in scientific research because cracks can lead to catastrophic system failure. The purpose of structural fitness tracking is to diagnose and predict structural fitness. A complete crack detection method based on free vibration is widely used to find potential cracks in systems. However, static deflection methods are limited to predicting crack parameters. Therefore, this article uses the static deflection method to determine the crack locations and depth in the cantilever beam. A dead weight was attached to the beam’s free end, and two dial gauges were used. A gauge was attached to the free end of the beam to measure the free-end deflection. Another dial indicator was also installed near the crack to measure the static deflection of the crack. Numerical and experimental analyses were performed on 48 cracked specimens to measure the static deflection at two points. A regression model was developed to calculate the crack parameters, i.e., crack locations and crack depths in beams. To evaluate the reliability of the developed regression model, a machine learning model, i.e., Artificial Neural Network (ANN) and Random Forest (RF), was used for prediction. Regression, ANN, and RF models were developed using numerical and experimental datasets. The crack depth and location results obtained from the regression and machine learning models are consistent with the actual results. The crack parameters were predicted using static two-point deflection as input, and the results were encouraging. Therefore, the static two-point deflection approach may be widely used to detect future cracks in more complex structures.

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来源期刊
CiteScore
3.30
自引率
11.80%
发文量
203
期刊介绍: The aim of the Iranian Journal of Science and Technology is to foster the growth of scientific research among Iranian engineers and scientists and to provide a medium by means of which the fruits of these researches may be brought to the attention of the world’s civil Engineering communities. This transaction focuses on all aspects of Civil Engineering and will accept the original research contributions (previously unpublished) from all areas of established engineering disciplines. The papers may be theoretical, experimental or both. The journal publishes original papers within the broad field of civil engineering which include, but are not limited to, the following: -Structural engineering- Earthquake engineering- Concrete engineering- Construction management- Steel structures- Engineering mechanics- Water resources engineering- Hydraulic engineering- Hydraulic structures- Environmental engineering- Soil mechanics- Foundation engineering- Geotechnical engineering- Transportation engineering- Surveying and geomatics.
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