.png){kind=icon}
Return
Defect-based building condition assessment
.png){kind=icon}
PRE
AI
DOI：10.1016/j.buildenv.2020.107575.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Building defects accelerates the deterioration of building condition leading to more frequent repairs with increased operating and maintenance costs up to 4% or more of total construction cost per annum. Building condition assessments are carried out in order to identify defects and evaluate health status of building. However, existing assessment models are subjective, time consuming and tedious. To address the need for more objective and expeditious condition assessment this paper proposes a novel defect-based condition assessment model for existing concrete buildings considering both building physical and environmental condition. In order to deduce weighting coefficients for building defects Analytic Network Process (ANP) was used while severity of building defects is assessed using a grading scale. To incorporate uncertainty in judgement of inspection personnel, fuzzy membership functions were used to ascertain degree of belief in assessment. Evidential reasoning algorithm was used to aggregate and integrate different types of defects and to compute the overall condition assessment of building. This model is limited to concrete buildings only. The proposed model is implemented on BIM platform for exchange of information and better documentation during inspection. Proposed model was tested on a case study building and results were promising with organized inspection data management on a common BIM platform with potential to expedite inspection process while managing large amount of inspection data on handheld tablet.
Keywords:
Building condition assessment
Analytic network process
Fuzzy set theory
Evidential reasoning
Building information modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.4W
Citations:
6.6W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveReassessment of Cronobacter spp. originally isolated as Enterobacter sakazakii from infant food
PREAI
ShareSaveShareSaveShareSaveShareSave
View more