Structural Health Monitoring (SHM) is essential to identifying problems that may cause human and material losses produced by disastrous and unexpected structural failures. The dynamic characterization of civil structures adopting SHM systems provides, at a particular moment, accurate information about the current structural response. When considering a proper SHM strategy and affordable economic investment for the maintenance required by structures, it is possible to safeguard the original structural parameters and avoid potential failures. However, traditional SHM systems are not desirable due to the large economic investments involved and demand for exhausting fieldwork. This research presents the design, implementation, and validation of a low-cost SHM system composed of a Wireless Sensor Network (WSN) and a base station in order to identify dynamic properties from the measured structural response. A set of small- and full-scale experimental tests were conducted to validate the WSN system using a wired traditional SHM system. Vibration data recorded through the low-cost SHM system were processed and compared to determine the natural frequencies, mode shapes, and damping properties of different structures by computing modal identification techniques, frequency analysis, and logarithmic decrement. The obtained dynamic characterization results from small- and full-scale experimental testing conclude that the low-cost SHM system can reliably identify the dynamic properties of the tested structures. This proposal provides an inexpensive, functional, and innovative alternative for SHM regarding traditional monitoring systems.

Simplifications and theoretical assumptions are usually incorporated into the numerical modeling of structures. However, these assumptions may reduce the accuracy of the simulation results. This problem has led to the development of model-updating techniques to minimize the error between the experimental response and the modeled structure by updating its parameters based on the observed data. Structural numerical models are typically constructed using a deterministic approach, whereby a single best-estimated value of each structural parameter is obtained. However, structural models are often complex and involve many uncertain variables, where a unique solution that captures all the variability is not possible. Updating techniques using Bayesian Inference (BI) have been developed to quantify parametric uncertainty in analytical models. This paper presents the implementation of the BI in the parametric updating of a five-story building model and the quantification of its associated uncertainty. The Bayesian framework is implemented to update the model parameters and calculate the covariance matrix of the output parameters based on the experimental information provided by modal frequencies and mode shapes. The main advantage of this approach is that the uncertainty in the experimental data is considered by defining the likelihood function as a multivariate normal distribution, leading to a better representation of the actual building behavior. The results showed that this Bayesian model-updating approach effectively allows a statistically rigorous update of the model parameters, characterizing the uncertainty and increasing confidence in the model’s predictions, which is particularly useful in engineering applications where model accuracy is critical.

In this research, two components were developed jointly: On the one hand, an experimental plan was created to obtain specific variables of the concrete and serve as a reference for the second model, a numerical and computational type created to address the variability in parameters, such as the elasticity and flow of coarse aggregate and mortar. The experimental work reproduced a specific gradation with a 1” nominal maximum size TMN on spheroidal-shaped particles. To address the diversity of limestone in the national territory, rocks were extracted from five calcareous areas of the country, which were transformed through different activities from lamination and turning with different bits to being carved into spheres until the reference gradation formed. The next stage consisted of determining the mechanical and stress–strain properties of both the mortar and coarse aggregate. In the case of mortar, the compressive stress was obtained from 50 mm × 50 mm × 50 mm cubes and the modulus of elasticity from 100 mm × 200 mm cylinders; for the coarse aggregate, the compressive stress was obtained through tests on cylinders of the calcareous rock used to form the spherical aggregate particles. The materials were mixed according to a previous proportioning for f′_c = 28 MPa and cast in cylinders of 100 mm × 200 mm. Finally, the compressive stress and the modulus of elasticity in these specimens were determined. Separately, a computational model was created to reproduce the experimental model with the same type of materials and load conditions and thereby estimate the compressive stress and modulus of elasticity of the tested material. This model was developed based on finite elements simulating concrete under a two-phase model, in which the coarse aggregate phase was arranged with spheroidal particles that were assembled with the mortar paste, thus finally reproducing a concrete cylinder of 100 mm × 200 mm. Material properties, taken from experimental work, were assigned to these materials. Initially, work was carried out in the elastic range, obtaining, as a result, the modulus of elasticity E_c, and then the specimen was brought to failure, obtaining, as a result, the maximum compressive stress f′_c. To attend to the influence of the effect of the mesh size for modeling on the numerical results of both parameters, several simulations were carried out in which mesh sizes of 4.0, 3.5, 3.0, and 2.5 mm were established for the mortar and the coarse aggregate, respectively, for carrying out the modeling. The results in the computational model showed that the compressive stress turned out to be more sensitive than the modulus of elasticity to the variation in the size of the mesh. For the first, the differences between the 4 mm mesh and the 2.5 mm mesh reached 3%, but for the second, the difference only reached 1% between the results for the same meshes. When the results between the experimental and computational models were compared, we found that the experimental values had the best closeness with results in the 2.5 mm mesh.