Defect-based building condition assessment

被引:36
|
作者
Faqih, Faisal [1 ]
Zayed, Tarek [1 ]
机构
[1] Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Peoples R China
关键词
Building condition assessment; Analytic network process; Fuzzy set theory; Evidential reasoning; Building information modeling; INDOOR ENVIRONMENTAL-QUALITY; INSPECTION; COMFORT; PRODUCTIVITY; DIAGNOSIS; GENDER;
D O I
10.1016/j.buildenv.2020.107575
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Defect-Based Condition Assessment of Concrete Bridges Fuzzy Hierarchical Evidential Reasoning Approach
    Moufti, Sami A.
    Zayed, Tarek
    Abu Dabous, Saleh
    TRANSPORTATION RESEARCH RECORD, 2014, (2431) : 88 - 96
  • [2] Defect-Based Condition Assessment Model for Sewer Pipelines Using Fuzzy Hierarchical Evidential Reasoning
    Daher, Sami
    Zayed, Tarek
    Hawari, Alaa
    JOURNAL OF PERFORMANCE OF CONSTRUCTED FACILITIES, 2021, 35 (01)
  • [3] A Hybrid Multi-Criteria Decision Making Model for Defect-Based Condition Assessment of Railway Infrastructure
    El-khateeb, Laith
    Mohammed Abdelkader, Eslam
    Al-Sakkaf, Abobakr
    Zayed, Tarek
    SUSTAINABILITY, 2021, 13 (13)
  • [4] Defect-Based Testing
    Pretschner, Alexander
    DEPENDABLE SOFTWARE SYSTEMS ENGINEERING, 2017, 50 : 141 - 163
  • [5] Defect-Based Testing
    Pretschner, Alexander
    DEPENDABLE SOFTWARE SYSTEMS ENGINEERING, 2015, 40 : 224 - 245
  • [6] Defect-based testing for fabless companies
    Khare, J
    Heineken, HT
    2000 IEEE INTERNATIONAL WORKSHOP ON DEFECT BASED TESTING, PROCEEDINGS, 2000, : 23 - 29
  • [7] Probabilistic Defect-Based Risk Assessment Approach for Rail Failures in Railway Infrastructure
    Jamshidi, Ali
    Roohi, Shahrzad Faghih
    Nunez, Alfredo
    Babuska, Robert
    De Schutter, Bart
    Dollevoet, Rolf
    Li, Zili
    IFAC PAPERSONLINE, 2016, 49 (03): : 73 - 77
  • [8] Defect-based structural integrity in nuclear plants
    Nikbin, Kamran
    STRUCTURAL INTEGRITY IN NUCLEAR ENGINEERING, 2011, : 19 - 19
  • [9] BUILDING INFORMATION MODELING (BIM)-BASED BUILDING CONDITION ASSESSMENT: A SURVEY OF WATER PONDING DEFECT ON A FLAT ROOF
    Ani, Adi Irfan Che
    Johar, Suhana
    Tawil, Norngainy Mohd
    Abd Razak, Mohd Zulhanif
    Hamzah, Noraini
    JURNAL TEKNOLOGI-SCIENCES & ENGINEERING, 2015, 75 (09): : 25 - 31
  • [10] Defect-based fatigue model for additive manufacturing
    Shukri Afazov
    Ahmad Serjouei
    Graham J. Hickman
    Rajan Mahal
    Damien Goy
    Iain Mitchell
    Progress in Additive Manufacturing, 2023, 8 : 1059 - 1066