Searcharxiv⌕ Search

arXiv · 2610.00056

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

Abstract

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ochok Duangsano, Kuo-Chieh Chao, Sompote Youwai, Nattavich Sittiamornporn, Chana Phutthananon, Pornkasem Jongpradist. 2026-09-04. Agentic AI for Staged Three-Dimensional Finite-Element Tunnel Modelling. https://arxiv.org/abs/2610.00056

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Differentiable Hybrid Modelling for Learning and Optimising Chemical Transport Processes from Experimental Data

Transport models underpin the design and optimisation of chemical processes, yet their constitutive laws are typically hand-picked, their experimental initial conditions are assumed exact, and their computational cost restricts optimisation to a handful of process variables. Here we introduce a differentiable hybrid modelling framework that embeds neural networks within a GPU-accelerated population balance equation solver to discover constitutive laws and assimilate initial conditions directly from experimental crystallisation data. Using thirteen real experiments with multi-dimensional size and shape measurements, we show that treating measured initial conditions as exact yields a physically inconsistent rate law despite improved predictive accuracy, whereas assimilating them recovers a physically consistent, non-linear law and reduces trajectory error sevenfold relative to conventional modelling. End-to-end differentiability further enables gradient-based optimisation of high-dimensional operating profiles, identifying a 300-variable temperature profile that reduces crystal aspect ratio by 25% at constant mass, facilitating superior product attributes. Our framework augments, rather than replaces, mechanistic modelling, offering a general route to learning transport models from data and optimising processes with them.

cs.CE↗

Inspect Any Crack: An Automated End-to-End Crack Segmentation and Severity Assessment Framework

Image-based crack analysis is essential for automated infrastructure inspection to reduce the cost, subjectivity, and safety risks of manual surveys. Despite advances in visual crack detection, methods that translate predictions into physically meaningful and engineering-interpretable crack condition information remain limited. To bridge this gap, this study proposes Inspect Any Crack (IAC), an innovative end-to-end crack recognition and condition assessment framework that automatically derives physical geometric measurements and continuous severity scores from concrete-crack imagery. At its core, IAC introduces SACv2-Tiny, an efficient crack segmentation model adapted from a vision foundation model. Trained on the challenging OmniCrack30K dataset, SACv2-Tiny achieved a $62.49\%$ F1-score and $45.45\%$ IoU on the test set and a mean zero-shot F1-score of $69.89\%$ across three unseen datasets, demonstrating robust segmentation that enables downstream analysis with limited post-processing. IAC further processes the predicted masks to quantify physical crack width and affected area using ground-sampling-distance calibration. Validation at 41 caliper-referenced locations yielded a mean relative width error of $7.33\%$. Beyond geometric quantification, IAC introduces a novel continuous crack-condition metric, the Crack Severity Index (CSI), bounded on $[0,100]$, which combines maximum crack width and affected-area ratio into a unified, standards-informed severity measure. Through these developments, IAC advances crack inspection beyond pixel-level recognition toward interpretable and automated infrastructure condition assessment.

cs.CE↗

Adaptive Encoding Strategy for Quantum Annealing in Mixed-Variable Engineering Optimization

Mixed discrete-continuous optimization is central to engineering design, where discrete choices interact with continuous fields. These problems are difficult due to high-dimensional, complex search spaces. To tackle them, Quantum Annealing (QA) is promising, yet its native binary nature supports only discrete variables, making accurate and efficient encodings of continuous quantities a central challenge. Existing approaches either split the coupled problem, mapping discrete decisions to QA while solving continuous fields classically, or use fixed-bit-depth encodings. The former compromises QA's global search advantages; the latter can underrepresent dynamic range or inflate the number of binary variables. We show that simply increasing bit depth can even degrade performance on current QA hardware, underscoring the need for alternative encodings. In response, we introduce an adaptive encoding strategy for continuous variables in QA that enables efficient treatment of coupled mixed-variable problems. We propose an update strategy for the representable ranges of the continuous variables and demonstrate its utility by integrating it into the minimum complementary energy formulation for structural design optimization, which provides a single, coupled constrained problem. We apply a quadratic penalty method where we update the representation of the continuous variables while targeting the full original objective, preserving QA's global search capability. On a published benchmark, the size optimization of a composite rod, our adaptive encoding improves solution quality under a fixed binary variable budget, demonstrating a superior precision-resource trade-off. Since the framework generalizes beyond structural design, it offers practical guidance for encoding continuous variables for QA and indicates that adaptive representations can enhance precision on current hardware.

cs.CE↗