The　interpretation　of　the　cone　penetration　test　(CPT)　still　relies　largely　on　empirical　correlations　that　have　been　predominantly　developed　in　resource-intensive　and　time-consuming　calibration　chambers.　This　paper　presents　a　CPT　virtual　calibration　chamber　using　deep　learning　(DL)　approaches,　which　allow　for　the　consideration　of　depth-dependent　cone　resistance　profiles　through　the　implementation　of　two　proposed　strategies:　(1)　depth-resistance　mapping　using　a　multilayer　perceptron　(MLP)　and　(2)　sequence-to-sequence　training　using　a　long　short-term　memory　(LSTM)　neural　network.　Two　DL　models　are　developed　to　predict　cone　resistance　profiles　(qc)　under　various　soil　states　and　testing　conditions,　where　Bayesian　optimization　(BO)　is　adopted　to　identify　the　optimal　hyperparameters.　Subsequently,　the　BO-MLP　and　BO-LSTM　networks　are　trained　using　the　available　data　from　published　datasets.　The　results　show　that　the　models　with　BO　can　effectively　improve　the　prediction　accuracy　and　efficiency　of　neural　networks　compared　to　those　without　BO.　The　two　training　strategies　yielded　comparable　results　in　the　testing　set,　and　both　can　be　used　to　reproduce　the　whole　cone　resistance　profile.　An　extended　comparison　and　validation　of　the　prediction　results　are　carried　out　against　numerical　results　obtained　from　a　coupled　Eulerian-Lagrangian　(CEL)　model,　demonstrating　a　high　degree　of　agreement　between　the　DL　and　CEL　models.　Ultimately,　to　demonstrate　the　usability　of　this　new　virtual　calibration　chamber,　the　predicted　qc　is　used　to　enhance　the　preceding　correlations　with　the　relative　density　(Dr)　of　the　sand.　The　improved　correlation　with　superior　generalization　has　an　R2　of　82%　when　considering　all　data,　and　89.6%　when　examining　the　pure　experimental　data.　©　2024　Institute　of　Rock　and　Soil　Mechanics,　Chinese　Academy　of　Sciences