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研究生:余孟霖
研究生(外文):Meng-Lin Yu
論文名稱:Machine learning-based system for PCCES project data auto-correction
論文名稱(外文):Machine learning-based system for PCCES project data auto-correction
指導教授:蔡孟涵蔡孟涵引用關係
指導教授(外文):Meng-Han Tsai
口試委員:郭榮欽謝佑明蔡明達
口試日期:2020-07-22
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:營建工程系
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:63
中文關鍵詞:自然語言處理詞嵌入
外文關鍵詞:Natural Language ProcessingWord Embedding
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According to the administrative order of the Taiwan government, public construction projects with a budget of more than NTD$ ten million should use public construction cost estimation system (PCCES) to formulate project budgets and the PCCES coding accuracy rate should be at least 40%. Although PCCES plays an important role in Taiwan's public construction projects, it is full of incorrect data due to different user habits, time-consuming system operation, and long system operation pretraining, etc. Therefore, this study proposed a natural language processing (NLP) and machine learning-based text classification system, an automatic correction system (ACS), to correct the wrong data in PCCES automatically. The ACS has two significant features: data correction which converts unstructured data into structured data; a recommendation function which provides users a recommendation list for manual data correction. In ACS, a language model was trained based on machine learning techniques that could deal with natural languages. With this model, the system can classify user input and then return answers based on the classification results. The ACS utilized the continuous bag of words (CBOW) model to find the most similar data. Subsequently, the ACS replaces the user input with the obtained result. Users can also correct data manually by ACS’s recommendation list based on the results. For implementation, the developed system was used to correct the real construction data in PCCES’s database. The results show that the system can correct 18,511 pieces of data with an accuracy of 76%. Additionally, this study conducted a user test by asking the subject to conduct same tasks by using both ACS and PCCES. The results showed that the recommendation function could reduce the operation time by 51.69% as compared with the original system. By these tests, the system was validated to be able to shorten the user's operating time and eliminate the impact of the user’s familiarity with PCCES.
Abstract iv
Acknowledgements v
Table of Contents vi
List of Figures viii
List of Tables ix
1. Introduction 1
1.1 Data management 1
1.2 Public construction cost estimation system 2
1.3 Benefits and challenges of the PCCES 5
2. Literature review 7
2.1 Challenges in construction data management 7
2.2 Unstructured data processing 9
2.3 Machine learning-based methods 11
3. Objective 12
4. Methodology 13
4.1 System overview 13
4.2 Data processing module 14
4.2.1 Raw data collection 15
4.2.2 Text processor 15
4.3 Search processing module 16
4.3.1 Word embedding 17
4.3.2 Similarity calculation 19
4.4 Mapping process module 20
5. Implementation 21
5.1 Training data 21
5.1.1 System manuals 21
5.1.2 Blank valuations 24
5.2 Module implementation 25
5.2.1 Text processing 25
5.2.2 Database 27
5.2.3 Model training 29
5.2.4 Searching 30
5.2.5 Mapping 31
6. Validation 32
6.1 System evaluation 32
6.1.1 Data source 33
6.1.2 Classification 34
6.1.3 Result and discussion 34
6.2 User test 35
6.2.1 Users 36
6.2.2 Test scenario 36
6.2.3 Result and discussion 38
7. Discussion 42
7.1. Contributions 42
7.2. Limitations 45
8. Conclusion 46
References 47
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