Probabilistic modeling framework for prediction of seismic retrofit cost of buildings

Nasrazadani, H ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1061/(ASCE)CO.1943-7862.0001354
  3. Abstract:
  4. This study presents a framework that utilizes Bayesian regression to create probabilistic cost models for retrofit actions. Performance improvement is the key parameter introduced in the proposed framework. The incorporation of this novel feature facilitates the characterization of retrofit cost as a continuous function of the desired performance improvement. Accounting for the performance gained from retrofit enables the use of the models in determining the optimal level of retrofit. Furthermore, accounting for the model uncertainty facilitates the use of the models in risk and reliability analyses. The proposed framework is applied to create seismic retrofit cost models for masonry school buildings in Iran. A cost database of 167 masonry retrofit projects was compiled and used to create cost models for three retrofit actions, namely, Shotcrete, fiber-reinforced polymer, and steel belt. The proposed framework identifies the most influential variables that govern building retrofit cost. Practitioners can use the proposed framework to create cost models for various retrofit actions to decide whether to retrofit a building and to identify the least costly retrofit action. © 2017 American Society of Civil Engineers
  5. Keywords:
  6. Probabilistic model ; Quantitative methods ; Retrofit cost ; Costs ; Fiber reinforced plastics ; Masonry materials ; Network function virtualization ; Seismology ; Steel fibers ; Uncertainty analysis ; Bayesian regression ; Continuous functions ; Fiber reinforced polymers ; Model uncertainties ; Probabilistic costs ; Probabilistic modeling ; Quantitative method ; Risk and reliability analysis ; Retrofitting
  7. Source: Journal of Construction Engineering and Management ; Volume 143, Issue 8 , 2017 ; 07339364 (ISSN)
  8. URL: https://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001354