A Neuro-Symbolic Framework for Ensuring Deterministic Reliability in AI-Assisted Structural Engineering: The SYNAPSE Architecture
Ing. Adriano Castagnone 1, Ing. Giuseppe Nitti 1,2,*
1 S.T.A. DATA Srl, Via Saluzzo, 71 – 10126, Torino, Italia
2 Politecnico di Torino, Corso Duca degli Abruzzi, 24 – 10129 Torino, Italia
* Recapiti: castagnone@stadata.com (A. Castagnone), giuseppe.nitti@polito.it (G. Nitti)
Abstract
This paper addresses the opportunities and risks of integrating Large Language Models (LLMs) into structural engineering. Exclusive reliance on LLMs is inadequate in this field, because their probabilistic nature can lead to hallucinations and inaccuracies that are unacceptable in safety-critical domains which require rigorous calculations. To resolve this dilemma, we propose adopting Neuro-Symbolic Artificial Intelligence (NSAI), a hybrid approach that balances neural intuition with symbolic rigor. The NSAI architecture employs an intelligent query system to enrich user requests and delegate critical operations to deterministic external algorithms. This system is designed to enhance reliability and support regulatory compliance, as exemplified by the 3Muri chatbot case study, an NSAI (gemini-2.5-flash)-based intelligent assistant for structural analysis software. We developed 3Muri chatbot implementing AI processes. Our experimental results, based on over 200 questions submitted to the chatbot, show that this hybrid approach achieves 94% accuracy while keeping response times below 2 s. These results validate the feasibility of deploying AI systems in safety-critical engineering domains.
