The　public　opinion＇s　growing　awareness　of　ensuring　enough　safety　level　of　existing　structures　was　sadly　promoted　by　recent　iconic　collapses,　for　instance,　the　Morandi　viaduct　collapse　in　Genoa　in　2018　among　others.　Therefore,　the　current　historical　period　represents　an　important　catalyst　for　an　unprecedented　renewal　period　in　the　entire　civil　engineering　sector,　especially　devoted　to　research　and　development　of　smart　structural　health　monitoring　(SHM)　systems　and　artificial　intelligence　(AI)　data-driven　supported　solutions.　The　monitoring　of　structural　health　can　be　formally　pursued　at　five　different　investigation　levels.　The　foremost　represents　the　screening　damage　detection　task　which　is　of　crucial　importance　to　optimize　time　and　economic　resources　to　further　deeper　investigation　level　for　understanding　the　nature　of　actual　structural　damage.　Indeed,　an　effective　damage　identification　procedure　permits　to　promptly　intervene　with　targeted　maintenance　and　safety　restoration　actions.　In　this　study,　the　authors　analyzed　the　effects　of　different　noise　levels　on　a　recently　proposed　AI-assisted　data-driven　damage　detection　procedure　based　on　the　analysis　of　output-only　vibrational　response　data　collected　from　accelerometer　sensors　deployed　on　civil　structures.　Specifically,　three　reasonable　signal-to-noise　ratio　(SNR)　levels,　viz.　20dB,　40dB,　and　60　dB,　have　been　calibrated　in　order　to　simulate　existing　low-cost　Internet　of　Things　(IoT)　sensors　currently　available　on　the　market　for　smart　monitoring　solutions,　i.e.　focusing　on　micro-electro-mechanical　system　(MEMS)　accelerometer　sensors.　The　current　preliminary　numerical　results　showed　that　the　chosen　AI-based　damage　detection　procedure　presents　a　fairly　good　noise　immunity　performance,　with　97%　of　accuracy　for　1D　convolutional　neural　network　(1D-CNN)　model　considering　three　noise　levels.　©　2024　IEEE.