PSI - Issue 62

Fabio Severino et al. / Procedia Structural Integrity 62 (2024) 276–284 Severino et al. / Structural Integrity Procedia 00 (2019) 000–000

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demarcation between data from IoT devices, user decisions, and AI evaluations reduces the liability of infrastructure managers utilizing AI for decision-making. Smart contracts executed by the hybrid blockchain are responsible for running the AI models, guaranteeing that the appropriate model is applied to the correct IoT measure from the blockchain. The outcome of this computation is subsequently recorded, creating a verifiable trail of the execution process. This allows any member of the network, or those with access to the exported data, to independently confirm the accuracy of the results. The smart contract can also be expanded to automate compliance and operational procedures. For example, it can trigger automated scheduling of inspections when specific conditions are detected or notify third-party auditors when predetermined conditions are met, streamlining the process of ensuring compliance and facilitating maintenance actions. 4. Conclusions In this paper we have discussed how integrating hybrid blockchain technology with AI techniques (both based on symbolic AI and Machine learning) endows automated solutions for the monitoring and assessment of existing civil infrastructures with a series of properties that collectively improve the trustworthiness of the entire process. In particular, our solution allows all actors in the monitoring process as well as external auditors and other social stakeholders to verify – at any future point in time – the entire trail of the process, from the training data used in the construction of an AI system, all the way down to the final assessment produced by the system. In so doing, our approach addresses the particularly delicate problem of establishing public trust in the care and diligence of all the actors involved in the maintenance in good working conditions of critical infrastructure which so heavily influence the daily life and operations of common citizens and economic actors alike. Braunschweig, B. and Ghallab, M., 2021. Reflections on AI for Humanity: Introduction. Reflections on Artificial Intelligence for Humanity, pp.1-12. Canciani, A., Felicioli, C., Lisi, A. and Severino, F., 2023. Hybrid DLT as a data layer for real-time, data-intensive applications. arXiv preprint arXiv:2304.07165. Canciani, A., Felicioli, C., Pelosi, A. and Severino, F., 2024. A Hybrid-DLT Based Trustworthy AI Framework, 31th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE-2023). Haber, S. and Stornetta, W.S., 1991. How to time-stamp a digital document (pp. 437-455). Springer Berlin Heidelberg. High-Level Expert Group on AI, 2019, Ethics Guidelines for Trustworthy Artificial Intelligence, https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai High-Level Expert Group on AI, 2020, Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment, https://digital-strategy.ec.europa.eu/en/library/assessment-list-trustworthy-artificial-intelligence-altai-self-assessment Hu, X., Wang, B. and Ji, H., 2013. A wireless sensor network ‐ based structural health monitoring system for highway bridges. Computer ‐ Aided Civil and Infrastructure Engineering, 28(3), pp.193-209. Jobin, A., Ienca, M. and Vayena, E., 2019. The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), pp.389-399. Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J. and Zhou, B., 2023. Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9), pp.1-46. Nakamoto, S., 2008. Bitcoin: A peer-to-peer electronic cash system. Decentralized business review. Natali, A., Messina, V., Salvatore, W., Gervasi, V., Anzalone, D., Canciani, A. and Severino, F., 2023. A new tailored developed software for the risk classification of bridges according to the Italian Guidelines. Procedia Structural Integrity, 44, pp.2012-2019. Sherman, A.T., Javani, F., Zhang, H. and Golaszewski, E., 2019. On the origins and variations of blockchain technologies. IEEE Security & Privacy, 17(1), pp.72-77. Wüst, K. and Gervais, A., 2018, June. Do you need a blockchain?. In 2018 crypto valley conference on blockchain technology (CVCBT) (pp. 45-54). IEEE. Xiao, Y., Zhang, N., Lou, W. and Hou, Y.T., 2020. A survey of distributed consensus protocols for blockchain networks. IEEE Communications Surveys & Tutorials, 22(2), pp.1432-1465. Zinno, R., Haghshenas, S.S., Guido, G., Rashvand, K., Vitale, A. and Sarhadi, A., 2022. The state of the art of artificial intelligence approaches and new technologies in structural health monitoring of bridges. Applied Sciences, 13(1), p.97. References