Damage Detection and Finite-Element Model Updating of Structural Components through Point Cloud Analysis

Ghahremani, Kasra; Khaloo, Alireza; Mohamadi, Sara; Lattanzi, David

doi:10.1061/(asce)as.1943-5525.0000885

Cited by 38 publications

(28 citation statements)

References 41 publications

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“…Once a remotely sensed defect in a structural component is detected through computer vision [34], the first step in the modeling process is to parameterize it so that a stochastic dynamic model can be reliably fit the extracted parameters, or a “feature vector” to track the defect evolution over time. The complex nature of point cloud data necessitates this low-dimensional parameterization, as tracking each individual point in a cloud would lead to an intractably high number of time-series model coefficients.…”

Section: Methodsmentioning

confidence: 99%

“…In complement to the expanding use of these technologies, there are now a variety of methods for isolating and extracting defects from 2D or 3D images [30,31], and advancements in deep machine learning methods portend future improvements [32,33]. A key advantage of these data sources is the direct link between quantified geometric changes and changes in the underlying mechanical performance that can be captured in finite element analysis, as evidenced by a variety of prior work [34,35,36]. While such capabilities provide valuable tools for structural assessment, they do not explicitly quantify life-cycle dynamics and forecasts of future defect conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Presented in this paper is a novel algorithm to model the life cycle of defects manifested as either 2D or 3D point clouds. Point cloud data are geometric representations of defects that can serve as a consistent record of the state of a structure at a given inspection interval and provide a basis for finite element analysis, among other uses [25,34]. This algorithm extracts latent features from these defect point clouds through computational geometry, fits a time-series process model to the evolution of those features, and uses a stochastic forecasting model to predict the future state of the defect.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, a predictive analysis with regard to the future condition of the structure could be carried out by linking the modeled evolution of defects to a numerical simulation, ultimately helping to provide a complete representation of structural performance and integrity over time. This paper does not consider the predictive simulation aspect of this process, and the readers are referred to References [34,35,36,46] for potential applications in this domain.…”

Section: Introductionmentioning

confidence: 99%

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Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting

Mohamadi

Lattanzi

2019

Sensors

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The evaluation of geometric defects is necessary in order to maintain the integrity of structures over time. These assessments are designed to detect damages of structures and ideally help inspectors to estimate the remaining life of structures. Current methodologies for monitoring structural systems, while providing useful information about the current state of a structure, are limited in the monitoring of defects over time and in linking them to predictive simulation. This paper presents a new approach to the predictive modeling of geometric defects. A combination of segments from point clouds are parametrized using the convex hull algorithm to extract features from detected defects, and a stochastic dynamic model is then adapted to these features to model the evolution of the hull over time. Describing a defect in terms of its parameterized hull enables consistent temporal tracking for predictive purposes, while implicitly reducing data dimensionality and complexity as well. In this study, two-dimensional (2D) point clouds analogous to information derived from point clouds were firstly generated over simulated life cycles. The evolutions of point cloud hull parameterizations were modeled as stochastic dynamical processes via autoregressive integrated moving average (ARIMA) and vectorized autoregression (VAR) and compared against ground truth. The results indicate that this convex hull approach provides consistent and accurate representations of defect evolution across a range of defect topologies and is reasonably robust to noisy measurements; however, assumptions regarding the underlying dynamical process play a significant the role in predictive accuracy. The results were then validated on experimental data from fatigue testing with high accuracy. Longer term, the results of this work will support finite element model updating for predictive analysis of structural capacity.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting

Mohamadi

Lattanzi

2019

Sensors

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“…Wang et al (2018a) investigate a damage identification technique based on the Laplace transform-based spectral element method (LTSEM) and strain statistical moment (SSM). Ghahremani et al (2018) present a localized methodology for the automatic and systematic detection and quantification of damage in structural components using high-fidelity three-dimensional (3D) point cloud data, followed by a corresponding local update to a finite-element (FE) model. Xiong et al (2018) investigate structural dynamical monitoring to identify the presence of bridge scouring through tracing the changes of vibration characteristics induced by scour.…”

mentioning

confidence: 99%

Damage Detection and Condition Assessment of Civil Structures

2020

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Automated finite element modeling and analysis of cracked reinforced concrete beams from three dimensional point cloud

Zhang

Shooshtarian

et al. 2021

Structural Concrete

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Modeling and assessing the load-carrying capacity of reinforced concrete (RC) structures that are cracked is important because the condition of an RC structure changes due to natural aging and accumulated damage. The current approach to determining the residual capacity of a cracked RC structure is timeconsuming and error-prone due to a limited capacity to obtain and process structural information. In this study, an automated approach was proposed to determining residual capacity by performing finite element (FE) analysis of an RC structure using point cloud three dimensional modeling. Two simply supported RC beams were subjected to bending tests to provide validation data. The initial activity was to extract dimensional data from laser-scanned point cloud data of a beam. The information obtained was used to automatically generate nonlinear FE models of the beam that was used for nonlinear FE analyses in Abaqus. An initial crack was added to the beam to model a cracked beam in real conditions. Three different models were used to simulate initial cracks, a weakened element model, a cohesive zone model, and an extended FE model.The results indicate that the proposed method of automating the combination of point cloud data with nonlinear FE analysis provides a novel practical approach to modeling and assessing cracked RC beams.

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Damage Detection and Finite-Element Model Updating of Structural Components through Point Cloud Analysis

Cited by 38 publications

References 41 publications

Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting

Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting

Damage Detection and Condition Assessment of Civil Structures

Automated finite element modeling and analysis of cracked reinforced concrete beams from three dimensional point cloud

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