Analysis of the Complex Mechanisms of Defect Generation in Construction Projects

Jassmi, Hamad Al; Han, Sangwon; Davis, Steven

doi:10.1061/(asce)co.1943-7862.0001042

Cited by 26 publications

(14 citation statements)

References 40 publications

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“…Thus, Aljassmi et al [6] proposed pathogenicity, which is a cause's ability to trigger other high-magnitude conditions, as an additional criterion to tackle the challenge, and introduced a complementary approach which captures causality of defect causes and quantifies the most influential causes in terms of pathogenicity. As an extension, Aljassmi et al [14] further identified the most influential defect causes on the basis of the frequency, magnitude and pathogenicity by conducting a questionnaire survey of 106 industry professionals to grasp defect causes and their causality. While this approach has great potential to discover what to manipulate in order to minimize construction defects, it is still difficult to elucidate vagueness or subjectivity regarding defect causality because the causality is identified through a questionnaire survey.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Extending the theoretical foundation proposed by [20], this paper aims to systematically identify and quantify interrelated causality among construction defects through analysis of 2949 defect instances observed in finishing work which has a decisive impact on the overall quality of a building. In addition, since this paper analyzes actual defect instances carefully reported by practitioners, instead of using a questionnaire survey, it is expected to obtain relatively objective results, and thus to overcome the vagueness and subjectivity issue raised to Aljassmi et al [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Through a literature review and the analysis of the defect data with contractors, 21 defect causes were finally identified as shown in Table 5. Interference by Other Tasks [3,14] In this process, inevitable factors, such as weather and problems generated from design phase, are excluded. As shown in Table 5, some defect causes identified through a literature review look very similar to each other (e.g., C1 and C2).…”

Section: Data Collectionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects

et al. 2019

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A construction defect can cause schedule delay, cost overrun and quality deterioration. In order to minimize these negative impacts of construction defects, this paper aims to analyze the causality of construction defects. Specifically, association rule mining (ARM) is used to quantify the interrelationships between defect causes, and social network analysis (SNA) is utilized to find out the most influential causes triggering generation of construction defects. The suggested approach was applied to 2949 defect instances in finishing work. Through this application, it was confirmed that the proposed approach can systematically identify and quantify causality among defect causes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

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Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects

et al. 2019

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“…Some previous studies have examined the causes and origins of construction defects (Atkinson 1999;Josephson, Hammarlund 1999;Love, Edwards 2004 Forcada et al 2013a;Aljassmi et al 2016;Shirkavand et al 2016); some others have classified defects by type (Love, Irani 2003;Chew 2005;Karim et al 2006;Mills et al 2009;Ahzahar et al 2011;Forcada et al 2013b). However, these studies are limited in the statistical data analyses of defect classification and defect cause distribution; the statistical methods used in them are not capable of automated data explorations.…”

Section: Data Miningmentioning

confidence: 99%

Examining Association Between Construction Inspection Grades and Critical Defects Using Data Mining and Fuzzy Logic

Lin

Fan²

2018

Journal of Civil Engineering and Management

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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.

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“…Various methods have been proposed to analyze the causes of defects [2]- [5]. Among them, data mining (DM) is a procedure for extracting useful information from data, analogous to digging for an ore in a mine.…”

Section: Introductionmentioning

confidence: 99%

Decision Tree Analysis of the Relationship between Defects and Construction Inspection Grades

Lin¹

2019

IJMMM

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Defects are an important indicator of project quality; moreover, eliminating defects is a key objective of project management. Therefore, using the appropriate analytical tools and methods, training and testing the defect data, and selecting the best algorithm for the defect feature are important. These steps can directly reveal the decision rules for each defect, and they can assist in determining key approaches to construction site management for effective defect prevention. In this study, a model obtained by using the chi-squared automatic interaction detection (CHAID) algorithm was validated, and its prediction benefits were calculated. A total of 499 defect types were retrieved from the Public Construction Management Information System in Taiwan and used as the foundation of a statistical analysis of 990 construction projects with 17,648 construction defects. First, a cluster analysis of inspection scores and defect frequencies was performed to reclassify and establish a new grade. Next, five rules were established for using the decision tree to classify defects and inspection grades. Finally, results revealed that the prediction accuracy of the CHAID algorithm was 75.45%. The five rules can be used for defect management and prevention strategies.

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Analysis of the Complex Mechanisms of Defect Generation in Construction Projects

Cited by 26 publications

References 40 publications

Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects

Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects

Examining Association Between Construction Inspection Grades and Critical Defects Using Data Mining and Fuzzy Logic

Decision Tree Analysis of the Relationship between Defects and Construction Inspection Grades

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