ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example
This study aims to develop an automatic data correction system for correcting the public construction data. The unstructured nature of the construction data presents challenges for its management. The different user habits, time-consuming system operation, and long pretraining time all make the data...
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doaj-c11484e9ad8d41faa1c7d3744637cc1e2021-01-04T00:00:53ZengMDPI AGSustainability2071-10502021-01-011336236210.3390/su13010362ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data ExampleMeng-Lin Yu0Meng-Han Tsai1Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 106, TaiwanDepartment of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 106, TaiwanThis study aims to develop an automatic data correction system for correcting the public construction data. The unstructured nature of the construction data presents challenges for its management. The different user habits, time-consuming system operation, and long pretraining time all make the data management system full of data in an inconsistent format or even incorrect data. Processing the construction data into a machine-readable format is not only time-consuming but also labor-intensive. Therefore, this study used Taiwan’s public construction data as an example case to develop a natural language processing (NLP) and machine learning-based text classification system, coined as automatic correction system (ACS). The developed system is designed to automatically correct the public construction data, meanwhile improving the efficiency of manual data correction. The ACS has two main features: data correction that converts unstructured data into structured data; a recommendation function that provides users with a recommendation list for manual data correction. For implementation, the developed system was used to correct the data in the public construction cost estimation system (PCCES) in Taiwan. We expect that the ACS can improve the accuracy of the data in the public construction database to increase the efficiency of the practitioners in executing projects. The results show that the system can correct 18,511 data points with an accuracy of 76%. Additionally, the system was also validated to reduce the system operation time by 51.69%.https://www.mdpi.com/2071-1050/13/1/362natural language processingconstruction data managementmachine learning |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Meng-Lin Yu Meng-Han Tsai |
spellingShingle |
Meng-Lin Yu Meng-Han Tsai ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example Sustainability natural language processing construction data management machine learning |
author_facet |
Meng-Lin Yu Meng-Han Tsai |
author_sort |
Meng-Lin Yu |
title |
ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example |
title_short |
ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example |
title_full |
ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example |
title_fullStr |
ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example |
title_full_unstemmed |
ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example |
title_sort |
acs: construction data auto-correction system—taiwan public construction data example |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-01-01 |
description |
This study aims to develop an automatic data correction system for correcting the public construction data. The unstructured nature of the construction data presents challenges for its management. The different user habits, time-consuming system operation, and long pretraining time all make the data management system full of data in an inconsistent format or even incorrect data. Processing the construction data into a machine-readable format is not only time-consuming but also labor-intensive. Therefore, this study used Taiwan’s public construction data as an example case to develop a natural language processing (NLP) and machine learning-based text classification system, coined as automatic correction system (ACS). The developed system is designed to automatically correct the public construction data, meanwhile improving the efficiency of manual data correction. The ACS has two main features: data correction that converts unstructured data into structured data; a recommendation function that provides users with a recommendation list for manual data correction. For implementation, the developed system was used to correct the data in the public construction cost estimation system (PCCES) in Taiwan. We expect that the ACS can improve the accuracy of the data in the public construction database to increase the efficiency of the practitioners in executing projects. The results show that the system can correct 18,511 data points with an accuracy of 76%. Additionally, the system was also validated to reduce the system operation time by 51.69%. |
topic |
natural language processing construction data management machine learning |
url |
https://www.mdpi.com/2071-1050/13/1/362 |
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