The operation of buildings aims to ensure their safe and economical use throughout their lifespan. However, the importance of operational management in building facilities is sometimes underestimated. Cases of damage or deterioration often indicate unresolved maintenance issues or the absence of effective forecasting, especially in environments exposed to dynamic external influences. These shortcomings can lead to management decisions that fail to account for critical factors influencing a building’s long‑term structural and operational performance. One significant challenge, closely linked to these considerations, is the impact of traffic‑induced vibrations on buildings located near roads. Traffic-induced vibrations can challenge building operations, causing plaster scratching and cracking, plaster detachment, structural damage, or even building collapse. Measuring dynamic actions on real structures is labor-intensive, costly, and not always justified. Therefore, the primary objective of this research was to develop a predictive algorithm for assessing the risk of adverse dynamic impact on residential buildings. Artificial Neural Networks and Bayesian Networks have been employed to forecast the effects of traffic-induced vibrations. This paper presents a detailed performance analysis of these systems and the validation of the proposed assessment model. The verification process involved in situ measurements on structures identified as being at risk from paraseismic effects. This study introduces an integrated approach combining Bayesian networks and artificial neural networks in an iterative mutual‑learning process, in which both models correct their internal parameters based on correct and incorrect classifications, thereby enhancing the stability of predictions and demonstrating its practical applicability through validation using real in situ measurements of traffic‑induced vibrations. Experimental results confirmed that the proposed methodology serves as an effective predictive tool in civil engineering. To mitigate the effects of vibrations on buildings, the study explored both active and passive protection methods. The choice of protection method depends on several factors, including whether the building and road are existing or planned, and the specific conditions for implementing vibration impact protection.