隨著網路社群與物聯網等技術的蓬勃發展所不斷產生與累積大量的數據，世界迎來了大數據的時代，各行各業都有應用大數據可能，藉由分析技術從大數據中找出各式應用的可能。另一方面，近年來隨著數據量的不斷增長與電腦計算能力之持續提升，已發展出了大數據分析的各式演算法如深度學習，深度學習在大數據預測分析之應用發揮了關鍵作用，也取得優秀成果，產品大量製造的高科技產業已成功將深度學習的技術應用於產能預測。營建產業之產品具有客製化之特性，施作過程較少有數位化的資料紀錄與自動化的機器加工，且多倚賴大量人力，因此大數據分析技術的應用在營建產業有其困難度，鋼構廠是營建產業中少有較具固定生產流程與標準化製造模式的供應商，具潛力作為大數據分析應用之可能的研究目標。故本研究嘗試大數據分析應用於鋼構廠柱一次生產線可能，以電腦模擬的技術針對鋼構廠柱一次生產線建立虛擬工廠，虛擬工廠可模擬鋼構廠生產線的作業流程，再配合對收集多個實際鋼結構BIM模型案例所拆解出大量鋼構構件所構成的桿件池做隨機取樣，所產生大量的生產案例配置，即可透過所建立的虛擬工廠模擬出這些大量案例的生產過程履歷，再以深度學習技術基於大量的模擬案例預測鋼構廠之產能，並與機器學習方法之預測結果比較，對大數據分析應用於鋼構廠產能預測的可能性進行初探。

With the vigorous development of the Internet community and the Internet of Things, a large amount of data has been continuously generated and accumulated. The world has ushered in the era of big data. It is possible to apply big data in various fields and use analysis techniques to find out the possibilities of various applications from big data. On the other hand, with the continuous increase of data volume and the continuous improvement of computer computing power in recent years, various algorithms for big data analytics such as deep learning have been developed. Deep learning has played a key role in the application of big data analytics for predictive analysis, and has also achieved excellent results. The high-tech industry, which manufactures products in large quantities, has successfully applied deep learning technology for predicting production capacity. The productions of the construction industry have the characteristics of customization, lacking of digital data records and machinery automation in the construction process, and relying on a lot of manpower. Therefore, the application of big data analytics in the construction industry has its difficulties. The steel structure factory is one of the few suppliers in the construction industry that has a relatively fixed production process and a standardized manufacturing model. Thus, it has the potential to be a possible research target for the application of big data analytics. Therefore, this research explores the possibility of applying big data analytics to the box column production line of a steel structure factory. Computer simulation technology is adopted to establish a virtual factory for the box column production line of a steel structure factory. Several steel structure BIM models are disassembled to produce a large number of steel structure members that form a pool of components. By randomly sampling the components in the pool, a large number of production case configurations can then be automatically generated. The production processes of these cases can obtained by simulation using the established virtual factory. Deep learning is then applied to predict the production capacity of a steel structure factory based on these simulated cases. The prediction results and compare with the results of machine learning methods to explore the possibility of applying big data analysis to the production capacity prediction of a steel structure factory.

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