Examining association between construction inspection grades and critical defects using data mining and fuzzy logic
This paper explores the relations between defect types and quality inspection grades of public construction projects in Taiwan. Altogether, 499 defect types (classified from 17,648 defects) were found after analyzing 990 construction projects from the Public Construction Management Information Syste...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Vilnius Gediminas Technical University
2018-06-01
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| Series: | Journal of Civil Engineering and Management |
| Subjects: | |
| Online Access: | http://journals.vgtu.lt/index.php/JCEM/article/view/3072 |
| _version_ | 1818381163999264768 |
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| author | Chien-Liang Lin Ching-Lung Fan |
| author_facet | Chien-Liang Lin Ching-Lung Fan |
| author_sort | Chien-Liang Lin |
| collection | DOAJ |
| description | This paper explores the relations between defect types and quality inspection grades of public construction projects in Taiwan. Altogether, 499 defect types (classified from 17,648 defects) were found after analyzing 990 construction projects from the Public Construction Management Information System of the public construction commission which is a government unit that administers all the public construction. The core of this research includes the following steps. (1) Data mining (DM) was used to derive 57 association rules which altogether contain 30 of the 499 defect types. (2) K-means clustering was used to regroup the 990 projects of two attributes (defect frequency and original grading score of each project) into four new quality classes, so the 990 projects can be more evenly distributed in the four new classes and the correctness and reliability of the following analyses can be ensured. (3) Finally analysis of variance (ANOVA), fuzzy logic, and correlation analysis were used to verify that the aforementioned 30 defect types are the important ones determining inspection grades. Results of this research can help stakeholders of construction projects paying more attention on the root causes of the critical defect types so to dramatically raise their management effectiveness. |
| first_indexed | 2024-12-14T02:30:13Z |
| format | Article |
| id | doaj.art-9cb5c85f668f49fd815b2e89459be110 |
| institution | Directory Open Access Journal |
| issn | 1392-3730 1822-3605 |
| language | English |
| last_indexed | 2024-12-14T02:30:13Z |
| publishDate | 2018-06-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Journal of Civil Engineering and Management |
| spelling | doaj.art-9cb5c85f668f49fd815b2e89459be1102022-12-21T23:20:17ZengVilnius Gediminas Technical UniversityJournal of Civil Engineering and Management1392-37301822-36052018-06-0124410.3846/jcem.2018.3072Examining association between construction inspection grades and critical defects using data mining and fuzzy logicChien-Liang Lin0Ching-Lung Fan1Department of Construction Engineering, National Kaohsiung University of Science and Technology, KaohsiungInstitute of Engineering Science and Technology, National Kaohsiung University of Science and Technology & Department of Civil Engineering, the Republic of China Military Academy, KaohsiungThis paper explores the relations between defect types and quality inspection grades of public construction projects in Taiwan. Altogether, 499 defect types (classified from 17,648 defects) were found after analyzing 990 construction projects from the Public Construction Management Information System of the public construction commission which is a government unit that administers all the public construction. The core of this research includes the following steps. (1) Data mining (DM) was used to derive 57 association rules which altogether contain 30 of the 499 defect types. (2) K-means clustering was used to regroup the 990 projects of two attributes (defect frequency and original grading score of each project) into four new quality classes, so the 990 projects can be more evenly distributed in the four new classes and the correctness and reliability of the following analyses can be ensured. (3) Finally analysis of variance (ANOVA), fuzzy logic, and correlation analysis were used to verify that the aforementioned 30 defect types are the important ones determining inspection grades. Results of this research can help stakeholders of construction projects paying more attention on the root causes of the critical defect types so to dramatically raise their management effectiveness.http://journals.vgtu.lt/index.php/JCEM/article/view/3072data miningassociation rulesfuzzy setcritical defectsconstruction quality management |
| spellingShingle | Chien-Liang Lin Ching-Lung Fan Examining association between construction inspection grades and critical defects using data mining and fuzzy logic Journal of Civil Engineering and Management data mining association rules fuzzy set critical defects construction quality management |
| title | Examining association between construction inspection grades and critical defects using data mining and fuzzy logic |
| title_full | Examining association between construction inspection grades and critical defects using data mining and fuzzy logic |
| title_fullStr | Examining association between construction inspection grades and critical defects using data mining and fuzzy logic |
| title_full_unstemmed | Examining association between construction inspection grades and critical defects using data mining and fuzzy logic |
| title_short | Examining association between construction inspection grades and critical defects using data mining and fuzzy logic |
| title_sort | examining association between construction inspection grades and critical defects using data mining and fuzzy logic |
| topic | data mining association rules fuzzy set critical defects construction quality management |
| url | http://journals.vgtu.lt/index.php/JCEM/article/view/3072 |
| work_keys_str_mv | AT chienlianglin examiningassociationbetweenconstructioninspectiongradesandcriticaldefectsusingdataminingandfuzzylogic AT chinglungfan examiningassociationbetweenconstructioninspectiongradesandcriticaldefectsusingdataminingandfuzzylogic |