Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM
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 mainten...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/9691868 |
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| _version_ | 1832559885702660096 |
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| author | Daopeng Wang Jifei Fan Hanliang Fu Bing Zhang |
| author_facet | Daopeng Wang Jifei Fan Hanliang Fu Bing Zhang |
| author_sort | Daopeng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2b4e4e9848454376887f398cded87da2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2b4e4e9848454376887f398cded87da22025-02-03T01:28:52ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/96918689691868Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTMDaopeng Wang0Jifei Fan1Hanliang Fu2Bing Zhang3School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Management, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaConstruction 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.http://dx.doi.org/10.1155/2018/9691868 |
| spellingShingle | Daopeng Wang Jifei Fan Hanliang Fu Bing Zhang Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM Complexity |
| title | Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM |
| title_full | Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM |
| title_fullStr | Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM |
| title_full_unstemmed | Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM |
| title_short | Research on Optimization of Big Data Construction Engineering Quality Management Based on RNN-LSTM |
| title_sort | research on optimization of big data construction engineering quality management based on rnn lstm |
| url | http://dx.doi.org/10.1155/2018/9691868 |
| work_keys_str_mv | AT daopengwang researchonoptimizationofbigdataconstructionengineeringqualitymanagementbasedonrnnlstm AT jifeifan researchonoptimizationofbigdataconstructionengineeringqualitymanagementbasedonrnnlstm AT hanliangfu researchonoptimizationofbigdataconstructionengineeringqualitymanagementbasedonrnnlstm AT bingzhang researchonoptimizationofbigdataconstructionengineeringqualitymanagementbasedonrnnlstm |