應用人工智慧遷移學習預測營造工程物價指數

邱民翰

doi:10.6342/NTU202401182

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应用人工智慧迁移学习预测营造工程物价指数

Predicting Construction Cost Index using Artificial Intelligence and Transfer Learning

邱民翰 , 硕士　　导师：曾惠斌　　

繁体中文 DOI： 10.6342/NTU202401182

营建物价预测；迁移学习；预训练与微调；长短期记忆模型；技术分析指标； Construction cost forecasting ； Transfer Learning ； pre-training and fine-tuning ； LSTM ； Technical Indicator ； Technical Indicator ；

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Construction cost overruns and valuation disputes are common due to fluctuating material prices. Accurate predictions are crucial for forecasting bid prices, contract management, cost estimation, and procurement management. Despite advances in deep learning, its use in predicting construction costs has been limited. Most studies rely on construction cost indices or macroeconomic indicators for short-term forecasts, which are time-consuming and labor-intensive. This study explores the feasibility of technical analysis indicators for more accurate cost predictions and management. It proposes a framework using deep learning and transfer learning, specifically utilizing Long Short-Term Memory (LSTM) networks with pre-training and fine-tuning to predict both short-term and long-term construction cost indices in Taiwan. The research focuses on the general construction cost index, the cement and its products index, and the metal products index as target domains. Seven exchange rates are selected as initial source domains, and 14 source domain selection methods determine the final source domains. The model is based on a two-layer LSTM architecture, with pre-training and fine-tuning enhancing prediction performance. Results indicate that the final source domain for the general construction cost index and the cement and its products index is the New Taiwan Dollar to Japanese Yen exchange rate. In contrast, for the metal products index, it is the New Taiwan Dollar to Korean Won exchange rate. Pre-training and fine-tuning significantly improve prediction performance. Technical analysis indicators outperform macroeconomic indicators for the general construction cost index and the cement and its products index. This study presents a novel framework for predicting construction costs through pre-training and fine-tuning, marking the first application of this method in the field. This approach allows the model to better adapt to different markets, outperforming other machine learning models and providing more reliable long-term predictions. The study showcases technical analysis indicators in construction cost prediction, offering a more accurate method for cost management and project success.

参考文献 ( 100 ) 〈TOP〉

[1] 行政院主计总处. 营造工程物价指数, 2024.
[2] Akshay Tondak. Recurrent neural networks (rnn) tutorial: Rnn training, advantages disadvantages (complete guidance), 2023. June 23, 2023.
[3] Oinkina and Hakyll. Understanding lstm networks, 2015. August 27, 2015.
[4] S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359, 2010.
[5] L. Torrey and J. Shavlik. Transfer learning, pages 242–264. IGI Global, 2009.

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使用DOI作为永久连结

引用含有DOI的文献

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。 如想对DOI的使用与概念有进一步了解，请参考 （ ） 。

DOI可强化引用精确性、增强学术圈连结，并给予使用者跨平台的良好使用经验，目前在全世界已有超过五千万个对象申请DOI。如想对DOI的使用与概念有进一步了解，请参考（）。