1
|
Determining the Severity of Open and Closed Cracks Using the Strain Energy Loss and the Hill-Climbing Method. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Evaluating the integrity of structures is an important issue in engineering applications. The use of vibration-based techniques has become a common approach to assessing cracks, which are the most frequently occurring damage in structures. When involving an inverse method, it is necessary to know the influence of the position and the geometry of the crack on the modal parameter changes. The geometry of the crack, both in size and shape, defines the damage severity (DS). In this study, we present a method (DS-SHC) used for estimating the DS for closed and open transverse cracks in beam-like structures using the intact and damaged beam deflections under its weight and a Stochastic Hill Climbing (SHC) algorithm. After describing the procedure of applying DS-SHC, we calculate for a prismatic cantilever beam the severities for different crack types and depths. The results are tested by comparing the DS obtained with DS-SHC with those acquired from dynamic tests made using professional simulation software. We obtained a good fit between the severities determined in these two ways. Subsequently, we performed laboratory experiments and found that the severities obtained with the DS-SHC method can accurately predict the frequency changes due to the crack. Hence, these severities are a valuable tool for damage detection.
Collapse
|
2
|
Gillich N, Tufisi C, Sacarea C, Rusu CV, Gillich GR, Praisach ZI, Ardeljan M. Beam Damage Assessment Using Natural Frequency Shift and Machine Learning. SENSORS 2022; 22:s22031118. [PMID: 35161863 PMCID: PMC8839218 DOI: 10.3390/s22031118] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 11/16/2022]
Abstract
Damage detection based on modal parameter changes has become popular in the last few decades. Nowadays, there are robust and reliable mathematical relations available to predict natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF), and the artificial neural network (ANN), as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment was made in two steps. First, a coarse damage location was found from the networks trained for scenarios comprising the whole beam. Afterwards, the assessment was made involving a particular network trained for the segment of the beam on which the crack was previously found. Using the two machine learning methods, we succeeded in estimating the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which was the main goal of the practitioners, the errors were less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.
Collapse
Affiliation(s)
- Nicoleta Gillich
- Department of Engineering Science, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania; (N.G.); (C.T.); (Z.-I.P.)
| | - Cristian Tufisi
- Department of Engineering Science, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania; (N.G.); (C.T.); (Z.-I.P.)
| | - Christian Sacarea
- Department of Computer Science, Institute of German Studies, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania; (C.S.); (C.V.R.)
| | - Catalin V. Rusu
- Department of Computer Science, Institute of German Studies, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania; (C.S.); (C.V.R.)
| | - Gilbert-Rainer Gillich
- Department of Engineering Science, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania; (N.G.); (C.T.); (Z.-I.P.)
- Doctoral School of Engineering, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania;
- Correspondence:
| | - Zeno-Iosif Praisach
- Department of Engineering Science, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania; (N.G.); (C.T.); (Z.-I.P.)
- Doctoral School of Engineering, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania;
| | - Mario Ardeljan
- Doctoral School of Engineering, Babeș-Bolyai University, Str. M. Kogălniceanu 1, 400084 Cluj-Napoca, Romania;
| |
Collapse
|