## 4 Data Management
### Data Analysis Algorithms for Modern SHM Architecture  
The use of innovative sensor networks and the consequent production of a large amount of data has opened up the era of big data in the field of SHM. When dealing with large quantities of sensor data and a structure whose physical characteristics are complex or even unknown (which is very common in heritage structures), data-driven  
Figure 12: SHM-IoT system schematic (from [188]).  
models can be more effective in interpreting and analysing monitoring data than physics-based models [192]. Indeed, the data-driven approaches involve constructing a surrogate model from collected data that substitutes for the high-fidelity model to detect damage efficiently. Conversely, the physical-based model relies on applying analytical methods relating directly to physical parameters and implies a previous deep understanding of the structure, which must be fulfilled in historical constructions.  
In this context, the progress towards novel data-driven machine learning (ML) techniques has been gaining increasing attention. A systematic review of various ML techniques applied for SHM of heritage buildings has recently been presented by [PERSON] [191]. When dealing with optimisation problems, nature-inspired algorithms (e.g., genetic algorithm, auto-associative neural networks, etc.) turn out to be highly efficient for the SHM of historical structures [193,194].  
[PERSON] et al. [195] presented a monitoring technique based on the artificial neural network (ANN) to predict early risk warnings for historical buildings, with an application for a case study. The approach was based on analysing images of the same subject captured after proper periods. A signal alarm is sent when the ANN-based module recognizes anomalies between different images. The authors concluded that the proposed approach provided a valid system for detection and prediction.  
[PERSON] et al. [196] adopted a genetic algorithm to update a historic Civic Tower numerical model based on vibration-based identification results. The process consisted of updating the FE model's system matrices (mass, stiffness, and possibly damping matrices) until the difference between experimental and numerical modal results was minimized. Using a genetic algorithm allowed for overcoming the limitations associated with manual or approximate updating processes. This method demonstrated the efficiency of metaheuristics compared to time-consuming manual approaches and other automatic approaches unable to solve complex multidimensional optimisation problems.  
Once sensors are installed and values of the parameters are recorded, these algorithms allow for the implementation of an automatic process to overcome errors due to human decisions and to generate alerts. Typically, two naturally-inspired algorithms are used in the design of SHM for historic masonry structures: optimal sensor placement (OSP) and damage identification (DI) [197]. These algorithms are typically used to reduce the costs of maintenance of cultural heritage buildings because they can generate punctual and specific alerts, allowing for timely interventions only when and where necessary. Some future developments were also suggested, such as the introduction of an automatic detection technique.  
While the use of integrated monitoring systems comprising diverse sensing solutions is becoming a priority for effective local/global damage detection, novel solutions able to process a large amount of data from heterogeneous sensor networks need to be developed. [PERSON] and [PERSON] [198] developed two innovative software solutions, MOVA and MOSS, for integrated the long-term SHM of structures. These software programs enable the online system identification and damage detection of structures, including vibration-based SHM and data fusion of heterogeneous sensing systems with an innovative algorithm for automated anomaly detection.