Abstract
Construction industry is the largest data industry, but with the lowest degree of datamation. With the development and maturity of BIM information integration technology, this backward situation will be completely changed. Different business data from a construction phase and operation and a maintenance phase will be collected to add value to the data. As the BIM information integration technology matures, different business data from the design phase to the construction phase are integrated. Because BIM integrates massive, repeated, and unordered feature text data, we first use integrated BIM data as a basis to perform data cleansing and text segmentation on text big data, making the integrated data a “clean and orderly” valuable data. Then, with the aid of word cloud visualization and cluster analysis, the associations between data structures are tapped, and the integrated unstructured data is converted into structured data. Finally, the RNN‐LSTM network was used to predict the quality problems of steel bars, formworks, concrete, cast‐in‐place structures, and masonry in the construction project and to pinpoint the occurrence of quality problems in the implementation of the project. Through the example verification, the algorithm proposed in this paper can effectively reduce the incidence of construction project quality problems, and it has a promotion. And it is of great practical significance to improving quality management of construction projects and provides new ideas and methods for future research on the construction project quality problem.
Highlights
As large-scale, group-oriented, and complicated construction projects, especially large-scale cluster projects, are constructed, traditional project management theories, methods, and models could not fully meet the needs of actual management anymore
This research takes the quality of construction projects as the subject, and building information model (BIM) integrated construction engineering big data source as the foundation
This article is based on BIM integrating big construction data sources, extracting the text data related to the project quality, and carrying out data cleaning and text segmentation
Summary
As large-scale, group-oriented, and complicated construction projects, especially large-scale cluster projects, are constructed, traditional project management theories, methods, and models could not fully meet the needs of actual management anymore. In comparison with some architectural powerhouses such as Germany and the United States, China’s construction industry still suffers from low technical level, laborintensive and low-efficiency construction, and industrial chain fragmentation and other pain points This is a consequence of neglecting construction process big data. With the aid of big data mining technology and BIM, a systematic, refined, and information-based management model has been formed [3, 4] At this stage, most domestic companies are focusing on construction units to reduce construction costs and maximize profits. Compared with some building powers such as Japan, Germany, Complexity and the United States, the construction industry in China still suffers from pain spots of low technical level, labor intensiveness, low efficiency, and split industrial chain, which are all the causes for frequent project quality problems. In order to further improve project quality management and meet the needs of rapid economic development in the new era, we must strive to explore new management ideas and methods
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