Pornkasem Jongpradist

arXiv2026

Agentic AI for Staged Three-Dimensional Finite-Element Tunnel Modelling

Three-dimensional finite-element (FE) analysis gives the most complete picture of tunnelling-induced ground movement, but it is slow: an engineer must drive the PLAXIS 3D interface through project set-up, stratigraphy, materials, structural elements, meshing and staged calculation. This paper presents a pipeline in which a large language model (LLM) drives PLAXIS 3D through the Model Context Protocol (MCP), with no human operating the interface. A purpose-built MCP server exposes the official remote-scripting API as 19 typed tools. Four agents share the work: orchestrator, geometry, calculation and verification. They draw on 15 inspectable domain-knowledge skill modules and a provenance-tagged intake stage. The verification agent is information-barriered, auditing every model against the raw specification at a pre-mesh checkpoint and again as a calculation gate. It was evaluated on 12 single-tunnel problems in Bangkok subsoil across four capability tiers, using a 15-arm ablation matrix; 13 automated arms and a manual expert baseline were executed, one run per cell. The full system built 12 of 12 models at a mean of 6.1 min with a clean audit on every one; the same models took 24.6 min by hand. Domain skills were decisive: with the complete architecture but all 15 modules withheld, no model was produced. Base-model capability acted as a threshold rather than a gradient. Fable 5, Opus 5 and Sonnet 5 each built 12 of 12, while Haiku 4.5 halted on all 12. Among models that built, agreement was exact: 107 of 111 comparable cells reproduced the reference mesh to the element, and 83 of 86 solved cells agreed with the hand-built expert model within 1.01 % in maximum settlement and 1.21 % in trough-width parameter. Every exception traced to a value misread at intake. The reliability problem in agentic FE automation therefore lies in specification reading, not in code generation.

cs.CE↗

arXiv2025

Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers

This study presents an explainable parallel transformer architecture for soil liquefaction prediction that integrates three distinct data streams: spectral seismic encoding, soil stratigraphy tokenization, and site-specific features. The architecture processes data from 165 case histories across 11 major earthquakes, employing Fast Fourier Transform for seismic waveform encoding and principles from large language models for soil layer tokenization. Interpretability is achieved through SHapley Additive exPlanations (SHAP), which decompose predictions into individual contributions from seismic characteristics, soil properties, and site conditions. The model achieves 93.75% prediction accuracy on cross-regional validation sets and demonstrates robust performance through sensitivity analysis of ground motion intensity and soil resistance parameters. Notably, validation against previously unseen ground motion data from the 2024 Noto Peninsula earthquake confirms the model's generalization capabilities and practical utility. Implementation as a publicly accessible web application enables rapid assessment of multiple sites simultaneously. This approach establishes a new framework in geotechnical deep learning where sophisticated multi-modal analysis meets practical engineering requirements through quantitative interpretation and accessible deployment.

cs.LG↗