Singh, Akarsth KumarAkarsth KumarSinghPal, AritraAritraPalSHANG-HSIEN HSIEH2025-11-252025-11-2520259780645832228https://www.scopus.com/record/display.uri?eid=2-s2.0-105016544218&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/734131This research presents a two-phase AI-driven approach to enhance automated construction planning and scheduling, addressing common challenges such as project delays and budget overruns in the Architecture, Engineering, and Construction (AEC) industry, often caused by inefficient project management. Traditional AI methods rely heavily on static programming and manually annotated datasets, leading to inefficiencies. The proposed methodology uses prompt engineering to systematically extract and structure information from historical project schedules, creating datasets for training domain-specific Small Language Models (SLMs). In the first phase, fine-tuning SLMs, particularly mistral-7B, achieved 83.69% accuracy, outperforming traditional approaches. In the second phase, another fine-tuned SLM identified activity patterns, optimized sequencing, and predicted missing tasks, achieving a mean similarity score of 81.43%. The results demonstrate that structured prompt engineering significantly improves model accuracy and efficiency, reduces manual efforts, and addresses computational inefficiencies. This scalable, secure, and efficient approach sets a new standard in automated construction planning.trueActivity SequencingConstruction Planning and SchedulingDomain-Specific Instruct Fine-TuningPrompt EngineeringSmall Language Modelsconference paper10.22260/ISARC2025/01252-s2.0-105016544218