English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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%.

ACS: Construction Data Auto-Correction System—Taiwan Public Construction Data Example