Computational Intelligence via Artificial Neural Network-Particle Swarm Optimization for Multi-Directional Displacement Prediction in High-Rise Steel Diagrid Frames
Proceedings of the 2025 9th International Conference on Advances in Artificial Intelligence, (2026), pp. 261-267
Dante Laroza Silva
a
,
Kevin Lawrence M. De Jesus
b
,
Jimmy Catanes
c
,
Cirilo Mar Pat Gazzingan III
d
,
Crispin Lictaoa
e
,
Meriam Leopoldo
f
,
Junjun H. Moreno
b
a School of Civil Environmental, and Geological Engineering, Mapua University, Manila, Metro Manila, Philippines
b Department of Civil Engineering, FEU Institute of Technology, Manila, Metro Manila, Philippines
c Commission on Higher Education, Manila, Metro Manila, Philippines
d School of Graduate Studies, Mapua University, Manila, Metro Manila, Philippines
e Civil Engineering Department, Adamson University, Manila, Metro Manila, Philippines
f College of Engineering and Architecture, Mapua Malayan Colleges Mindanao, Davao City, Davao del Sur, Philippines
Abstract: Steel diagrid high-rise structures require repeated finite-element analyses to accurately predict the multi-directional displacements, which is a time-consuming approach for parametric exploration and early-stage design. This paper presents an artificial neural network (ANN) – particle swarm optimization (PSO) informed model for predicting multi-directional displacements of high-rise steel diagrid frames considering different parameters including the number of storeys (NS), diagrid angle (DA), cross-sectional area (CSA), total weight (TW), and mass of the diagrid exterior (MDE). The model was developed from a dataset of 360 simulations from SAP 2000 ranging from 20-80 storeys and 33.69°-90° angles was used to create a Levenberg-Marquardt (LM) ANN with hyperbolic tangent sigmoid (HTS) activation function and 11 hidden neurons. The PSO was integrated into the model to enhance the training by optimizing the weights and biases (WB) of the network. The ANN-PSO achieved excellent model performance results with R values ranging from 0.9931 to 0.9989 and mean squared error (MSE) ranging from 0.000380 to 0.017200. The sensitivity analysis (SA) utilizing Garson's algorithm (GA) revealed that the number of storeys and diagrid angles are primary influencing the X and Y-displacements while the total weight and cross-sectional area were the leading influential factors to the Z-displacement. The proposed ANN-PSO offers an accurate, interpretable and computationally efficient approach for performance-based preliminary design of steel diagrid high-rise structures.