Assessing the integrity of industrial and civil appliances has become a priority worldwide. Noteworthy, this goal requires a strong synergy between multiple tools, disciplines, and approaches to be attained via a joint hardware-software co-design of the different Structural Health Monitoring (SHM) system components. This work proposes the $\sf{MAC4PRO}$ architecture, a sensor-to-cloud monitoring platform that seamlessly integrates sensing and software technologies for accurate data measurement, transmission, and analysis. The developed solution stands out for its interoperability and versatility, making it a promising candidate for integration in the next generation of smart structures. Our platform was validated during extensive experimental campaigns targeted at various industrial scenarios. The results show that the $\sf{MAC4PRO}$ architecture can identify subtle changes, such as 1mm size leakage events in pipeline circuits, or less than 1% frequency drifts in civil buildings after seismic excitation, while ensuring more than 90% reduction in the edge-to-cloud data transfer process.

The growing importance of state assessments in civil engineering has led to intensive research into the development of damage identification methods based on vibrations. Natural frequencies and modal shapes have garnered great interest because modal parameters are invariant of structure. Moreover, thanks to the global nature of modal parameters, their variations are not limited to the location of the damage. This is an important advantage that offers the opportunity to identify damage with sensors whose position does not have to coincide with the damaged area. The integration of MEMS sensors into structural health monitoring (SHM) systems offers a promising approach to long-term structural maintenance, especially in large-scale infrastructure. This paper presents an anomaly detection technique that analyzes raw sequential data within a statistical framework to detect damage that causes prestress loss of the tendon by exploiting a distributed monitoring system composed of six high-performance MEMS sensors. The proposed system is preliminarily evaluated to identify the frequency of the first mode, and then the proposed methodology is validated on acceleration data collected on a 240 cm beam in three different damage configurations, achieving a high detection accuracy and showing that its output can also evaluate the damage localization.

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.