The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that complements language interpretation with structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 s), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation and regulatory automation.

The integration of large language models (LLMs) into structural engineering workflows presents both a transformative opportunity and a critical challenge. While LLMs enable intuitive, natural-language interactions with complex data, their limited arithmetic reasoning, contextual fragility, and lack of verifiability constrain their application in safety-critical domains. This study introduces a novel automation pipeline that couples generative AI with finite element modelling through the Model Context Protocol (MCP)—a modular, context-aware architecture that compliments language interpretation from structural computation. By interfacing GPT-4 with OpenSeesPy via MCP (JSON schemas, API interfaces, communication standards), the system allows engineers to specify and evaluate 3D frame structures using conversational prompts, while ensuring computational fidelity and code compliance. Across four case studies, the GPT+MCP framework demonstrated predictive accuracy for key structural parameters, with deviations under 1.5% compared to reference solutions produced using conventional finite element analysis workflows. In contrast, unconstrained LLM use produces deviations exceeding 400%. The architecture supports reproducibility, traceability, and rapid analysis cycles (6–12 seconds), enabling real-time feedback for both design and education. This work establishes a reproducible framework for trustworthy AI-assisted analysis in engineering, offering a scalable foundation for future developments in optimisation, and regulatory automation.

Modern engineering increasingly operates within socio-technical networks, such as the interdependence of energy grids, transport systems, and building codes, where decisions must be reliable and transparent. Large language models (LLMs) such as GPT promise efficiency by interpreting domain-specific queries and generating outputs, yet their predictive nature can introduce biases or fabricated values—risks that are unacceptable in structural engineering, where safety and compliance are paramount. This work presents a dataset that embeds generative AI into validated computational workflows through the Model Context Protocol (MCP). MCP enables API-based integration between ChatGPT (GPT-4o) and numerical solvers by converting natural-language prompts into structured solver commands. This creates context-aware exchanges—for example, transforming a query on seismic drift limits into an OpenSees analysis—whose results are benchmarked against manually generated ETABS models. This architecture ensures traceability, reproducibility, and alignment with seismic design standards. The dataset contains prompts, GPT outputs, solver-based analyses, and comparative error metrics for four reinforced concrete frame models designed under Ecuadorian (NEC-15) and U.S. (ASCE 7-22) codes. The end-to-end runtime for these scenarios, including LLM prompting, MCP orchestration, and solver execution, ranged between 6 and 12 s, demonstrating feasibility for design and verification workflows. Beyond providing records, the dataset establishes a reproducible methodology for integrating LLMs into engineering practice, with three goals: enabling independent verification, fostering collaboration across AI and civil engineering, and setting benchmarks for responsible AI use in high-stakes domains.

Reinforced concrete buildings with masonry-induced soft-storey irregularities exhibit extreme seismic vulnerability, a critical risk often underestimated by conventional code-based design. Standard equivalent static methods typically fail to capture the intense concentration of seismic demand at the flexible ground level, leading to unconservative designs that do not meet performance objectives. This research proposes a corrective linear–static methodology to address this deficiency. A new Equivalent Lateral Force profile (ELF_i1) was developed, derived from modal analyses of 235 representative soft-storey archetypes to accurately account for stiffness heterogeneity. This profile was integrated with a realistic response modification coefficient (R_i1 = 5.04), determined to be 37% lower than the normative R-factor (R = 8) prescribed by code. Nonlinear static analyses confirmed that conventional design resulted in “irreparable” damage (mean Global Damage Index = 0.82). In contrast, redesigning the structure using the proposed ELF_i1 and R_i1 methodology successfully mitigated damage concentration, upgrading structural performance to a “repairable” state (mean Global Damage Index = 0.52). Finally, Incremental Dynamic Analysis validated the approach; the redesigned structure satisfied FEMA P695 collapse prevention criteria, achieving an Adjusted Collapse Margin Ratio (ACMR) of 2.10. This study confirms the proposed method is a robust and practical design alternative for soft-storey mechanisms within a simplified linear framework.

Reinforced concrete (RC) buildings with masonry-induced soft-storey irregularities exhibit extreme seismic vulnerability, a critical risk often underestimated by conventional code-based design. Standard equivalent static methods typically fail to capture the intense concentration of seismic demand at the flexible ground level, leading to unconservative designs that do not meet performance objectives. This research proposes a corrective linear-static methodology to address this deficiency. A new Equivalent Lateral Force profile (ELF1) was developed, derived from modal analyses of 235 representative soft-storey archetypes. This profile was integrated with a more realistic response modification coefficient (Rᵢ₁ = 5.04), which was determined to be 37% lower than the normative R-factor (R=8) prescribed by code. Nonlinear static (pushover) analyses confirmed that the conventional design resulted in "irreparable" damage (mean GDI = 0.82). In contrast, redesigning the structure using the proposed ELF1 and Rᵢ₁ methodology successfully mitigated damage concentration, upgrading the structural performance to a "repairable" state (mean GDI = 0.52). Finally, Incremental Dynamic Analysis validated the approach; the redesigned structure satisfied FEMA P695 collapse prevention criteria, achieving an Adjusted Collapse Margin Ratio (ACMR) of 2.10. This study confirms the proposed ELF1 method is a robust and practical design alternative that accurately accounts for soft-storey mechanisms within a simplified linear framework.

Modern engineering faces an unprecedented paradox: while our systems grow in-creasingly complex, the tools we use to design and evaluate them must remain both reliable and transparent. Decisions in energy, infrastructure, and construction no longer occur in isolation but within socio-technical networks shaped by emerging technologies and artificial intelligence (AI). Among these advances, large language models (LLMs) such as GPT have attracted attention for their ability to synthesize solutions, interpret domain-specific queries, and generate outputs with minimal fine-tuning. Yet beneath this promise lies a critical flaw—LLMs do not compute; they predict. Their reliance on sta-tistical associations often leads to biases, logical missteps, or hallucinated values, short-comings that become especially problematic when applied to structural engineering, where safety and compliance are non-negotiable. This tension sets the stage for the present work. The dataset introduced here responds to this gap by demonstrating how generative AI can be grounded within validated com-putational workflows. Through the Model Context Protocol (MCP), ChatGPT was con-nected to numerical solvers such as OpenSees and benchmarked against ETABS, ensuring traceability, reproducibility, and compliance with seismic design standards. The dataset comprises technical prompts, GPT outputs, verified numerical analyses, and comparative error metrics for four reinforced concrete frame models designed under Ecuadorian (NEC-15) and U.S. (ASCE 7-22) standards. Beyond a simple record, it exemplifies a re-producible methodology for embedding LLMs within structural engineering practice. By curating and releasing this dataset, the study pursues three goals: to strengthen re-producibility by enabling independent verification, to foster interdisciplinary collabo-ration across AI, civil engineering, and data science, and to establish benchmarks for context-aware AI integration in high-stakes domains. In doing so, it not only illustrates the promise of human–AI teaming but also highlights the limitations that must be addressed if generative models are to be responsibly embedded in engineering decision-making.