In the ever-evolving landscape of technology, artificial intelligence (AI) has emerged as a transformative force, reshaping industries and redefining the way we approach problem-solving. While some may speculate about AI’s potential to replace humans, the reality is far more nuanced, especially in engineering. AI does not render engineers obsolete; instead, it serves as a catalyst for innovation, revolutionizing the engineering landscape in ways we could not have imagined. There is a symbiotic relationship between AI and engineers, which sheds light on the dynamic shifts occurring in the civil engineering industry and emphasizes the indispensable role that human expertise continues to play.
While there has been some speculation and concern that AI could replace human engineers, it’s crucial to recognize that AI is designed not to replace but to enhance human expertise. By automating repetitive tasks and offering valuable insights, AI empowers engineers to concentrate on complex problem-solving and innovation. Instead of replacing human workers, AI serves as a tool to boost efficiency and effectiveness in engineering tasks.
In structural health monitoring (SHM), our team has pioneered a transformative approach to harnessing AI, as detailed in progressive research papers. Developed by ASDEA hardware and ASDEA software, the MonStr system seamlessly integrates traditional processes with cutting-edge deep learning tools, offering a comprehensive method for monitoring and safeguarding structures. This innovative approach utilizes both software and hardware sensors, with the MonStr sensor network at its core, transforming data acquisition through MEMS technology. The MonStr sensors, developed by ASDEA, outperform conventional networks, providing unparalleled benefits in signal sampling, noise reduction, and synchronization management.
Highlighting the MonStr sensor network’s sophistication, it utilizes an open-source HDF5 format for signal management. To further enhance the reliability of SHM, the authors of “A Disruptive Strategy for Structural Health Monitoring with STKO” propose a hybrid solution. This strategy combines data-driven approaches with model-based techniques, introducing the concept of a digital twin (DT) to the structure. This workflow is integrated seamlessly within the STKO software framework and overcomes format incompatibilities, preventing data loss and streamlining the SHM process.
The holistic strength of this proposed solution lies in integrating MonStr devices, STKO, and Python tools. Together, these elements contribute to the robustness and efficiency of the innovative approach, showcasing the transformative impact of AI in advancing structural health monitoring. The integration of MonStr devices, STKO, and Python tools for parallel computing and the adoption of the HDF5 data format showcase the transformative impact of AI in enhancing structural health monitoring.
In the evolving landscape of civil engineering, the collaborative synergy between AI and engineering holds immense potential. Integrating AI technologies with established practices like building information modeling (BIM) and SHM systems offers a promising avenue for revolutionizing construction processes and improving efficiency, safety, and sustainability.
As illustrated in the paper “A Self-Consistent Artificial Intelligence-Based Strategy for Structural Health Monitoring” by L. Aceto et al.,
“Artificial Intelligence (AI) has become a standard tool for data analysis and prediction-making for problems arising in all scientific applications.”
This transformative role of AI, which seamlessly integrates with civil engineering and construction practices, aligns with the overarching theme of leveraging technology to advance SHM and the broader realm of civil engineering.
While AI technology arouses undeniable excitement and fascination, navigating it is far from simple. The construction sector poses distinctive challenges, from remote locations to unique complexities, making creative solutions imperative. This underscores the essential need for collaboration among civil engineers, programmers, and AI engineers, highlighting the importance of their joint efforts in overcoming the complex challenges within the industry.
L. Aceto, Alessia Amelio, Roberto Boccagna, Maurizio Bottini, Guido Camata, Nicola Germano, Massimo Petracca. (2022) A Self-Consistent Artificial Intelligence-Based Strategy for Structural Health Monitoring. Alessia Amelio, Roberto Boccagna, Maurizio Bottini, Guido Camata, Nicola Germano, Massimo Petracca, Giuseppe Quaranta. (2022) A disruptive strategy for structural health monitoring with STKO. Sofiat O. Abioye, Lukumon O. Oyedele, Lukman Akanbi, Anuoluwapo Ajayi, Juan Manuel Davila Delgado, Muhammad Bilal, Olugbenga O. Akinade, Ashraf Ahmed (2021) Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges.
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