Determining the Severity of Open and Closed Cracks Using the Strain Energy Loss and the Hill-Climbing Method

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.

Detection of transverse cracks in steel beams using damage location coefficients and artificial neural networks

“…The crack severity is evaluated using an energy-based approach, which states that a beam with a crack will have a reduced energy storage capacity compared to a similar crack-free beam [8]. Additionally, the cracked beam will experience greater deformation when subjected to a force [9]. Therefore, we can quantify the severity by utilizing a mathematical relationship [10]:…”

“…We implemented signal processing techniques, such as filtering and spectral analysis, to extract the relevant information related to the beam's natural frequencies. Furthermore, to complement the capabilities of LabVIEW and enhance the accuracy of the analysis, we employed previously developed software in Python [8,9]. This custom software, designed specifically for our research purposes, implemented advanced algorithms and statistical methods to further process the data and extract precise measurements of the natural frequencies.…”

“…The ANN is trained using a comprehensive database of known damage signatures created from DLC values, enabling accurate prediction of the position of cracks for new damage scenarios. The effectiveness of the ANN is evaluated through both FEM simulations and experimental measurements performed on a steel cantilever beam [8,9].…”

Damage detection in variable temperature conditions using artificial intelligence

“…The severity 𝛾 𝑎 for a given crack depth 𝑎 can be found by applying the method presented in ALEXANDRA-TEODORA AMAN, CRISTIAN TUFISI, GILBERT-RAINER GILLICH, TIBERIU MANESCU [16], and the squared normalized modal curvature for the double-clamped beam can be found using Eq. ( 9):…”

Detection of weak joints and damages for beams using machine learning

“…the transverse crack 𝛾 (𝑎 ). The method for determining 𝛾 (𝑎 ) is presented in the paper[14].In the current research, we determine the RFS values for the first eight weak-axis vibration modes for different damage scenarios, as shown in Fig.1. For perfect clamping the RFS for a damage located on inflection points and at the free end of the beam is 0, hence the horizontal axis for this case is marked with Min.…”