MS17.10 - Structural Health Monitoring

Understanding the dynamics of the interaction between railway vehicles and tracks is essential for forecasting condition and performing maintenance action to preserve the safety of railway infrastructure. In this work, physics-based models are deployed to predict the dynamic response of railway vehicles to track alignment and irregularities. Such models comprise a large number of parameters that need to be validated and possibly tuned; a task often accomplished on the basis of expert knowledge, without necessarily reflecting the condition of the operating system as-is. Uncertainties persist due to idealizations and approximative assumptions on the actual system behavior, as well as due to varying operational parameters (wheel profile, rail profile, rail moisture). In improving condition estimation capabilities, this study adopts a multi-body vehicle model, realized in SIMPACK software, and performs parameter tuning based on on-board measurement data (accelerations and forces) from an instrumented tilting train, which regularly traverses the Swiss Federal Railways (SBB) network. The pertinent vehicle model is optimized in terms of the interaction forces at the wheel-rail contact using a Bayesian model updating approach, relying on Markov Chain Monte Carlo (MCMC). The MCMC method is applied to estimate both vehicle parameters and their uncertainty, ultimately resulting in improved contact force estimations. We show that by applying MCMC, the mean squared error between simulated and measured forces can be significantly reduced, leading in an improved vehicle twin.

The problem of random vibration response-based damage detection for a set of 30 composite aerostructures under varying operating conditions and uncertainty is experimentally investigated. The employed aerostructures are 14ply carbon fiber C-shaped laminates, 860 mm long and rectangular (25x82 mm) cross-section. 20 aerostructures are in pristine condition, while 6 are subjected to square-shaped delamination of three distinct sizes (20, 40, 50 mm) and another 4 to impact-induced damage under three distinct energy levels (12.9, 17.9, 23.1 Joules). The experimental set-up involves rigid mounting of each aerostructure at one end, and (non-measurable) random force excitation at two distinct locations with the acceleration vibration response being measured at distinct locations. The excitation forces are random and of varying (per experiment) spectral characteristics simulating those potentially acting in flight, while temperature varies within the 0 - 25 oC range. Preliminary analysis of the measured vibration response signals obtained from the healthy set of pristine structures within the range of 0 – 2 kHz indicates rich dynamical information with strong uncertainty among the structures due to variability in manufacturing (inter-structural uncertainty) and to variability in the experimental set-up (mounting assembly and small discrepancies in the sensor and excitation locations). In fact, the uncertainty induced on the dynamics is further compounded by those of varying temperature and excitation profiles and is so strong as to significantly “mask” the effects of the considered damages, thus leading to a highly challenging damage detection problem. Damage detection is tackled via two unsupervised and robust data-driven vibration-response-only methods which are based on the Multiple Input Multiple Output (MISO) Transmittance Function in order to properly compensate for the effects of the varying excitation: The first is based on the Multiple Model (MM) and the second on the HyperSphere (HS) based novel frameworks. Each one builds a distinct approximation of the healthy subspace within a properly selected feature space, which is hereby defined as the space spanned by Principal Component Analysis (PCA) reduced and transformed parameter vectors of stochastic MISO AutoRegressive with eXogenous (ARX) excitation representations of the Transmittance Function estimated from the measured vibration signals. The methods are trained using vibration signals from 16 healthy structures under various excitation profiles and 6 distinct temperatures (Baseline Phase), while the performance is assessed with signals from the remaining 4 healthy and 10 damaged structures under different, from those used in the training, excitation profiles and temperatures (Inspection Phase). Damage detection assessment is based on thousands of Inspection Phase experiments with the results presented in terms of Receiver Operating Characteristic (ROC) curves. The two methods are shown to be capable of overcoming the challenges of the varying operating conditions and uncertainty, achieving impressive detection performance, characterized by a 97.5% correct detection rate for 5% false alarm rate, with the MM based method exhibiting a slight edge over its HS counterpart.

In recent years, several advanced technologies have been developed to automate inspections and monitoring processes of existing bridges [1]. Robotic platforms, as the Unmanned Aerial Vehicle (UAVs), and Artificial Intelligence (AI) techniques are capable of evaluating autonomously the actual mechanical performances of materials and structures reducing the inspection and maintenance costs. The research community has made significant progress in autonomous data acquisition, identification and localization of the damage through machine learning algorithms. In physics-based approach, the efficiency of the computational models, as for example those based on nonlinear finite element procedures, plays a crucial rule not only in the model updating of monitoring system but also in the training of deep neural network. Although the nonlinear structural response of the bridges can be efficiently analysed through two-dimensional and three-dimensional finite element (FE) models, these commonly require high computational efforts. The aim of this work is to present a high-performing 3D beam FE to model the nonlinear behaviour of reinforced concrete and prestressed reinforced concrete girders, including material degradation. In this regard, the adoption of a fiber cross-section discretization allows both to consider general material non-linearity, through the introduction of specific constitutive laws, and to identify the region of the beam section affected by the degradation mechanisms. A force-based (FB) formulation is adopted for the proposed beam element, that is proved to be more efficient than the classical displacement-based approach as it strictly satisfies equilibrium is along the element. In presence of material non-linearity, the computational advantages of the FB approach are relevant. A crucial aspect in presence of strain-softening material behaviour is the pathological mesh dependence of the FE numerical solutions. In this work, a proper regularization procedure for FB formulations is adopted to overcome this drawback [2]. The composite cross-section of the prestressed concrete girder is modelled in detail by considering concrete, reinforcing steel bars and prestressing tendons fibers. The nonlinear constitutive law of the concrete fibers is based on a plastic-damage model which considers two different damage parameters for the compression and tensile behaviour to take in account the re-closure of the tensile cracks, while a classical elasto-plastic constitutive law is adopted for steel bars and tendons. A predictor-corrector algorithm is used to solve the evolution problem of damage and plasticity. The presented force-based beam finite element based on a damage-plasticity model is implemented in the OpenSees framework. Some applications are computed to properly selected benchmarks to show the computational efficiency and the potentiality of the proposed modelling approach for nonlinear analysis repeated several times, such as in the application of the Artificial Intelligence for the assessment of existing bridges. [1] Spencer, B.F., V. Hoskere and Y. Narazaki. 2019. Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 5: 199–222 [2] Addessi, D., and V. Ciampi. 2007. A regularized force-based beam element with a damage-plastic section constitutive law. Int. J. Numer. Methods Eng., 70 (5): 610–629