Implementation of a deep neural network model to predict critical joint loads based on SAP2000 structural data - Ridwan, Ridwan; Setyawan, Ryan Ari; Fitriastuti, Fatsyahrina (Institute of Computer Science (IOCS), 2025)
This study propose s a De e p Ne ural Ne twork (DNN) frame work to pre dict joint re action force ratios in structural analysis using datase ts obtaine d from SAP2000 simulations. The datase ts cove r various load case s and ge ome trical parame te rs, e nsuring the mode l is e xpose d to dive rse structural sce narios. The DNN archite cture comprise s multiple fully conne cte d laye rs, e mploying Re LU activation functions, dropout re gularization, and batch normalization for stable training. Mode l pe rformance was e valuate d using Me an Square d E rror (MSE ), Me an Absolute E rror (MAE ), R² score , and pre diction accuracy within a 5% e rror margin critical for civil e ngine e ring applications. The re sults de monstrate e xce lle nt pre dictive capabilitie s, achie ving accuracy le ve ls e xce e ding 98% across all datase ts. Notably, the third datase t yie lde d the lowe st accuracy at 98.97% and an R² score of 0.9915, with slightly e le vate d e rror me trics (MSE of 5.11, RMSE of 2.26, and MAE of 1.51). De spite the se challe nge s, the DNN mode l consiste ntly de live rs robust pre dictions, showcasing its pote ntial for practical structural he alth monitoring and de sign optimization. Future work should conside r incorporating more dive rse and e xpe rime ntal data to e nhance mode l robustne ss furthe r.