Effects of Environmental Conditions on Historic Buildings: Interpretable Versus Accurate Exploratory Data Analysis

The goal of structural health monitoring is to continuously assess the structural integrity and performance of a building or structure over time. This is achieved by collecting data on various structural parameters and using this data to identify potential areas of concern or damage. A critical challenge involves some properties being severely damaged by recurrent variations of external factors. These variations in environmental and operational conditions (such as humidity, temperature, and traffic) can deflect the variability in structural behavior caused by structural damage and make it difficult to identify the damage of interest. In this paper, we present a study on how regression analysis and deep learning can be used to measure the influence of environmental factors on the structural behavior of the Leaning Tower of Pisa. Transparent linear regressors offer the benefit of being simple to understand and interpret. They can provide insights about the relationship between input an d target variables, as well as the relative importance of each input in forecasting the outcome. On the other hand, deep learning models are capable of learning nonlinear relationships between input and target variables. Definitively, in this work the accuracy-interpretability trade-off for structural health monitoring is discussed.