The remote sensing technique known as Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) allows the detection and monitoring of ground settlements, by generating deformation velocity maps and displacement time-series having centimeter to millimeter accuracy. These measurements can contribute to the evaluation of the structural conditions of the constructions. Given the settlements, different approaches exist for the assessment of the structural damage, ranging from empirical estimates to detailed finite element calculations. In this work, we integrate the results of a DInSAR analysis with an intermediate semi-empirical model to investigate three buildings located in the southern part of the city of Rome. The model, originally proposed by Finno et al. [(2005). ASCE Journal of Geotechnical and Geoenvironmental Engineering, 131(10), 1199–1210], considers each building as an equivalent laminated beam, where the layers represent the floors and the core material reproduces the infill walls. The results obtained by the model have been compared to the damages observed on the buildings, showing a good agreement and demonstrating that the proposed approach represents an effective and, at the same time, simple assessment tool for rapidly evaluating the conditions of several structures
In recent years, neural network models have been widely used in the Civil Engineering field. Interesting enhancements may be obtained by re-examining this model from the Bayesian probability logic viewpoint. Using this approach, it will be shown that the conventional regularized learning approach can be derived as a particular approximation of the Bayesian framework. Network training is only a first level where Bayesian inference can be applied to neural networks. It can also be utilized in another three levels in a hierarchical fashion: for the optimization of the regularization terms, for data-based model selection, and to evaluate the relative importance of different inputs. In this paper, after a historical overview of the probability logic approach and its application in the field of neural network models, the existing literature is revisited and reorganized according to the enunciated four levels. Then, this framework is applied to develop a two-step strategy for the assessment of the integrity of a long-suspension bridge under ambient vibrations. In the first step of the proposed strategy, the occurrence of damage is detected and the damaged portion of the bridge is identified. In the second step, the specific damaged element is recognized and the intensity of damage is evaluated. The Bayesian framework is applied in both steps and the improvements in the results are discussed.
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