Conference Agenda

High-fidelity numerical modeling plays a central role in structural health monitoring (SHM), particularly for materials such as glued laminated timber and masonry, which exhibit heterogeneity, anisotropy, complex boundary conditions, and environmental sensitivity. These characteristics often introduce considerable uncertainty in parameter estimation and model prediction and affect the results. Controlled laboratory testing offers an ideal environment for rigorous validation of probabilistic model updating techniques. This study implements a surrogate-assisted Bayesian model updating framework, with primary focus on a full-scale glued laminated timber beam tested under ambient vibration. A laboratory-scale two-span masonry arch bridge (MAB), constructed at the Universitat Politècnica de Catalunya within the PONT3 Project, is additionally considered to demonstrate transferability of the framework. Ambient vibration data were collected using accelerometers. Modal parameters were identified through output-only operational modal analysis (OMA) methods, and corresponding finite element (FE) models were built. Bayesian inference was performed using the Metropolis-Hastings Markov Chain Monte Carlo (MH MCMC) algorithm to estimate the posterior distributions of uncertain model parameters. The study also examines the influence of uncertain parameter selection on surrogate model accuracy for the timber beam. Gaussian Process surrogate models were trained using 1,000 FE simulations for each parameter set, and their accuracy was evaluated through 10-fold cross-validation. The comparison of two different datasets demonstrates that statistical definition of uncertain parameters significantly affects surrogate predictive capability, which in turn affects the stability of Bayesian updating and the credibility of the results. Posterior distributions of stiffness, density, and boundary parameters were obtained and compared for the timber beam. Fig. 1 presents the posterior distributions of model mechanical parameters and quantifies the associated uncertainty estimated based on the more accurate surrogate model. Improved surrogate performance resulted in more consistent posterior estimates and reduced uncertainty distribution. The same probabilistic framework was applied to the laboratory MAB using an accurate surrogate model, and the updating results are reported to demonstrate methodological transferability. The findings highlight the importance of appropriate uncertainty characterization in surrogate-assisted Bayesian model updating using ambient vibration data. Careful selection of prior parameter bounds enhances surrogate performance and improves the reliability of posterior parameter estimation using only ambient vibration data. The proposed framework provides a systematic basis for probabilistic model updating and uncertainty quantification in complex structural systems. This approach supports future applications in damage detection and structural health monitoring.

A 3D DISS FFEE model is proposed for the Colosseum area, with a 600x600x80 m patch of land having the anthropic, Holocene, Pleistocene, gravel and Pliocene layers, with the station, the Metro B and C tunnels, the Arch of Constantine. It is accepted that weak forces produce linear elastic behavior of the material. The elasticity modules for the elevation are obtained by experimental numerical comparison in the frequency domain. To obtain the forces produced by the trains in the intersections between tracks and sleepers, a multibody "car-track" model is used which are then applied to the 3D model to obtain the vibrations for the analytical-experimental comparison with the H/V diagrams of the terrain. The INGV catalogs report the most disastrous event that occurred in Rome in 1349, of over 7.5 MCS, from which a first southern breach occurred in the external ring; then there have been other intense seismic events in the past. Traffic vibrations can pose another long-term risk. For the SHM methodology, from the recordings of environmental vibrations it is possible to carry out identification by searching for fundamental frequencies and modal shapes, which are independent of the actions and instead change with damage to the masonry or with a structural modification. Afterwards, the characterization of the EEFF model can be carried out by varying the elasticity modules, until the best fitting between the experimental and numerical modes is obtained. The proposed SHM is appropriate and feasible, also to address problems such as those of the past (reinforcements, excavations, differential soil settlement).

For the past two years, EDF has been conducting an ambient vibration monitoring experiment on an arch dam. Based on experiments already conducted by EDF and those published in the literature, we will present the design (sensors, locations, etc.) and implementation of the experiment. Then, data processing with a large volume of data and including meteorological and classical monitoring data will be explained. The site chosen to conduct these measurements will be presented.

Data has been acquired for the past two years and the results show good detection of the first frequency modes and a strong intraday sensitivity. We present analyses of the sensitivity to water level, season, and we will focus on correlations with thermal fluctuations. Experiment is still running to analyse long term measurement.

The objective of this multi-year experiment is to calibrate numerical models of the dam using the measurements and to evaluate the benefits of long-term ambient vibration tracking for dam monitoring and analysis.

Structural Health Monitoring (SHM) plays a crucial role in assessing the safety and performance of structures, particularly after seismic events. This paper explores the use of a Micro-Electro-Mechanical Systems (MEMS) accelerometer for evaluating the integrity of structures through changes in their natural frequencies. The approach relies on the principle that damage or loss of stiffness within a structure leads to measurable shifts in its dynamic characteristics. A MEMS sensor was employed to record ambient vibration responses at different locations of the building, enabling the extraction of modal frequencies through signal processing techniques.

The study includes post-earthquake case studies where the MEMS sensor was used to measure frequency variations at selected points of reinforced concrete buildings. The observed changes in the natural frequency were interpreted as indicators of potential structural degradation. Despite using a single sensor, the approach proved effective in identifying variations in stiffness distribution and global structural condition. The results highlight the feasibility of rapid, low-cost structural assessment using compact MEMS-based instrumentation. This study demonstrates that even with minimal equipment, reliable insight into post-earthquake building integrity can be achieved, underscoring the potential of MEMS sensors as practical tools for field-based structural diagnostics and post-disaster evaluations.