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Open AccessArticle

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

Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA 22030, USA
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This paper is an expanded version of “A Computational Geometry Approach to the Life-Cycle Modeling of Remotely-Sensed Defects” published in the proceedings of the 2019 International Conference on Computing in Civil Engineering, Atlanta, GA, USA, 17–19 June 2019.
Sensors 2019, 19(20), 4571; https://doi.org/10.3390/s19204571
Received: 27 June 2019 / Revised: 27 September 2019 / Accepted: 1 October 2019 / Published: 21 October 2019
(This article belongs to the Section State-of-the-Art Sensors Technologies)
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. View Full-Text
Keywords: remote sensing; photogrammetry; life-cycle modeling; time-series forecasting; structural damage; stochastic modeling; convex hull; ARIMA; VAR; fatigue crack prediction remote sensing; photogrammetry; life-cycle modeling; time-series forecasting; structural damage; stochastic modeling; convex hull; ARIMA; VAR; fatigue crack prediction
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MDPI and ACS Style

Mohamadi, S.; Lattanzi, D. Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting. Sensors 2019, 19, 4571. https://doi.org/10.3390/s19204571

AMA Style

Mohamadi S, Lattanzi D. Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting. Sensors. 2019; 19(20):4571. https://doi.org/10.3390/s19204571

Chicago/Turabian Style

Mohamadi, Sara; Lattanzi, David. 2019. "Life-Cycle Modeling of Structural Defects via Computational Geometry and Time-Series Forecasting" Sensors 19, no. 20: 4571. https://doi.org/10.3390/s19204571

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