Large Language Models (LLMs) for Intelligent Construction Engineering and Management: Applications, Decision Support, and Responsible Implementation

Introduction

The construction engineering and management sector is entering a new phase of digital transformation driven by rapid advances in artificial intelligence, large language models (LLMs), multimodal foundation models, and agentic decision-support systems. Construction projects generate vast amounts of fragmented textual, numerical, visual, and procedural information, including contracts, specifications, safety reports, cost records, schedules, inspection logs, regulatory documents, BIM data, financial disclosures, and sustainability reports. However, much of this information remains underused because it is dispersed across project stages, organisations, platforms, and professional domains.

LLMs create new opportunities to transform fragmented construction information into actionable knowledge. Unlike conventional machine learning approaches that mainly focus on prediction, classification, or optimisation, LLMs offer capabilities in natural language reasoning, semantic retrieval, document intelligence, knowledge synthesis, human-AI interaction, and decision support. These capabilities are particularly relevant to construction engineering and management, where many critical decisions depend on interpreting complex documents, coordinating multiple stakeholders, understanding regulatory and contractual requirements, identifying risks, and converting past project knowledge into practical guidance.

Despite their potential, the application of LLMs in construction engineering and management remains at an early stage. Existing studies have begun to explore LLMs for safety report analysis, project information retrieval, planning support, contract interpretation, design coordination, and infrastructure data enrichment. However, substantial research gaps remain in domain adaptation, benchmark development, hallucination control, explainability, privacy protection, multimodal integration, responsible implementation, and validation in real construction environments. There is also a need to move beyond general demonstrations of LLM capability and examine how these models can be embedded into construction workflows to support reliable, accountable, and value-adding decision-making.

This Special Issue aims to provide a focused international forum for research on LLMs for construction engineering and management, with particular attention to knowledge, risk, and decision intelligence in the built environment. It welcomes theoretical, methodological, empirical, review, case-based, and policy-oriented contributions that investigate how LLMs can support construction safety and health management, cost and schedule control, contracts and dispute resolution, construction finance and organisational resilience, BIM and digital twins, lifecycle knowledge management, sustainability, circular economy, and SDG implementation.

By bringing together cutting-edge research from construction management, civil engineering, artificial intelligence, informatics, design, and sustainability, this Special Issue seeks to advance both scholarly understanding and practical implementation of LLM-enabled construction intelligence. Collectively, the contributions are expected to clarify the opportunities, limitations, governance requirements, and real-world impacts of LLMs, while offering evidence-based insights for researchers, practitioners, policymakers, and technology developers working toward safer, more efficient, resilient, and sustainable built environments.

List of Topic Areas

• LLMs for construction safety and health management, including accident report analysis, near-miss mining, hazard identification, safety knowledge extraction, safety training, and safety regulation interpretation.
• LLMs for construction cost, schedule, and project performance control, including cost estimate review, cost overrun diagnosis, schedule delay analysis, progress report summarization, productivity assessment, and early warning of project performance risks.
• LLMs for construction contracts, claims, and dispute resolution, including contract clause interpretation, change order analysis, claim document review, delay claim reasoning, dispute case retrieval, regulatory compliance checking, and risk allocation analysis.
• LLMs for construction finance, credit risk, and organizational resilience, including financial disclosure analysis, contractor credit scoring, insolvency risk identification, market risk monitoring, green finance evaluation, and construction firm resilience assessment.
• LLMs for BIM, digital twins, and lifecycle knowledge management, including natural language querying of BIM models, automated rule checking, project document retrieval, digital twin interaction, knowledge graph construction, design coordination support, and lifecycle information management.
• LLMs for sustainability, circular economy, and SDG implementation in construction, including lifecycle assessment support, sustainable procurement analysis, material reuse and waste reduction, carbon documentation, circular design knowledge retrieval, and decision support for accelerating progress toward the 2030 SDG agenda. 

Submission Information

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Key Dates

Opening date for manuscript submissions: 1 August 2026

Closing date for abstract submissions: 31 December 2026