# Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model

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## Abstract

**:**

## 1. Introduction

## 2. Review of Rapid Visual Screening Procedures

## 3. Type-2 Fuzzy Logic System

#### RVS Modeling Based on Interval Type-2 Fuzzy System

- Story number: Depending on the number of stories, one of the following classes should be selected. The classifications on this study are for mid-rise buildings as the number of stories less than 3 (Low), between 3 and 6 (Medium), and more than 6 (High) as presented in Figure 2a.
- Plan Irregularity: This parameter should be considered when any of the irregularities, for instance, buildings with re-entrant corners (L, T, U, E, + shape) and buildings with different lateral resistance in both directions, have been observed. Any asymmetrical plan and distribution of vertical elements can cause torsion to the building (Figure 2b).
- Vertical irregularity: If any of the irregularities such as steps in elevation view, inclined walls, buildings on a hill, soft story, buildings with short columns, and discontinuity in frames are identified, then this parameter should be considered (see Figure 2c).
- Age of building: This parameter is classified into three different input variable membership function as New (age <15 years), Moderate (15 < age < 30), and Old (age > 30) as presented in Figure 2d.
- Soil type: The soil type is classified into three different input variable membership function as A/B (rock and dense soil), C (stiff soil), and E (soft) as shown in Figure 2e.
- Peak Ground Velocity (PGV): The velocity is used to characterize the amplitude of seismic motion at intermediate frequencies therefore, it is useful to indicate the potential damage for structures sensitive to the field of intermediate frequencies [9,47]. In this paper, the PGV numerical values at any desired locations are based on the micro zoning studies by Sucuoglu and Yilmaz [48], which are fuzzified into three MFs as Low, Medium, and High, which are illustrated in Figure 2f.

## 4. Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Gaussian and generalized bell-shaped membership functions for input variables (

**a**) story number, (

**b**) plan irregularity, (

**c**) vertical irregularity, (

**d**) age, (

**e**) soil type, (

**f**) PGV.

**Table 1.**Summary of Fuzzy Logic application in visually earthquake hazard safety assessment of buildings.

Author(s) | Year | Studied Data | Purpose | Parameters | Fuzzy Inference |
---|---|---|---|---|---|

Ketsap et al. [15] | 2019 | Chiang Rai, Thailand | Earthquake risk evaluation of buildings by using Fuzzy risk model | Building occupancy (occupancy risk index), building vulnerability (FEMA 154 final score), Seismic hazard (PGA) | Hierarchical fuzzy rule-based |

Irwansyah et al. [24] | 2017 | 1450 (1000 modeling, 400 tests, 50 outlined) non-engineered buildings in Aceh, Indonesia | A three-stage fuzzy rule-based model to determine the hazard rate of building on the impact of earthquake for non-engineered houses | Structural (ring balk, floor block, column, foundation), non-structural (wall crack, wall cover, floor cover, tombak layer), ground condition (PGA, slope, fault distance) | Three-Stage Fuzzy Rule-Based |

Shahriar et al. [25] | 2012 | 43 Steel buildings in Northridge, USA | A risk-based seismic vulnerability assessment method using fuzzy-TOPSIS for damageability evaluation of steel buildings. | Structural system, vertical irregularity, plan irregularity, year of construction, construction quality, spectral acceleration | Fuzzy-TOPSIS, Mamdani |

Şen [26] | 2011 | 747 RC buildings in Istanbul, Turkey | Proposed a fuzzy logic model as supervised hazard center classification inference methodology for rapid and rational hazard classification. | Building height, soft height ratio, cantilever extension ratio, moment of inertia, frame number, column ratio, shear wall ratio, and PGV | Supervised fuzzy rule-based, Mamdani |

Şen [27] | 2010 | 1249 RC buildings in Istanbul, Turkey | Proposed a fuzzy logic model and software for rapid visual earthquake hazard evaluation of existing buildings. | Story number, cantilever extension, soft story, weak story, building quality, pounding effect, hill-slope effect, and PGV | Fuzzy rule-based, Mamdani |

Tesfamariam and Saatcioglu [4] | 2010 | 28 RC buildings in Bingöl, Turkey | Proposed a risk-based seismic vulnerability assessment based on fuzzy logic for prioritizing buildings for retrofit and repair. | Soft story, weak story, and short column effect, relative strength at joints, plan irregularity, torsional irregularity, diaphragm continuity, re-entrant corners, structural walls, construction and design quality, code enforcement, damage from previous earthquake, damage due to deterioration, relative height of slabs | Hierarchical fuzzy rule-based, Mamdani |

Tesfamariam and Saatcioglu [28] | 2008 | 93 RC (73 modeling and 20 test) buildings in Northridge, USA | Proposed a risk-based seismic vulnerability assessment based on FEMA154 and fuzzy logic for prioritizing buildings for retrofit and repair. | Structural system, plan irregularity, vertical irregularity, year of construction, construction quality, building importance and occupancy | Hierarchical fuzzy rule-based, Mamdani |

Moseley and Dritsos [29] | 2008 | 101 and 454 buildings in Athens, Greece | Proposed a fuzzy logic rapid visual screening procedure based on Greece method to improve the screening procedures | Same as below parameters | Hierarchical fuzzy rule-based, Sugano |

Demartinos and Dristos [30] | 2006 | 102 buildings in Athens, Greece | Fuzzy logic–based rapid visual screening procedure for categorization of buildings into five different types of possible damage with respect to the potential occurrence of a major seismic event. | Seismic hazard (ground motion, soil quality, building height), structural strength (building height, infill wall layout, soft story, short columns, design code), regularity (plan regularity, torsion possibility, height regularity, pounding possibility, plan regularity), structure’s condition (previous damage, maintenance) | Hierarchical fuzzy rule-based, Sugano |

Rule No. | Input Variable | Output (Damage) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

NS | PI | VI | Age | ST | PGV | ||||||||

1 | Low | and | No | and | No | and | Old | and | A/B | and | Low | Then | No damage |

… | … | … | … | … | … | … | … | … | … | … | … | … | … |

171 | Medium | and | Yes | and | No | and | Old | and | E | and | Medium | Then | Severe |

… | … | … | … | … | … | … | … | … | … | … | … | … | … |

324 | High | and | Yes | and | Yes | and | New | and | E | and | High | Then | Severe |

**Table 3.**Description of damage levels and recommended decision (adopted from [56]).

Damage State | Description | Decision |
---|---|---|

No damage | No damage, small cracks | Safe |

Low damage | Isolated non-structural damage, cracks in the interior walls or ceilings, damage in water lines, etc. | Slightly safe, might need small repair |

Moderate damage | Significant non-structural damage and slight structural damage | Moderate safe, needs repair and retrofitting |

Severe damage | Heavy non-structural damage and important structural damage | Slightly dangerous, need immediate repair and strengthening |

Collapse | Collapsed buildings or condemned to demolition | Dangerous, evacuation and demolish needed |

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**MDPI and ACS Style**

Harirchian, E.; Lahmer, T. Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model. *Appl. Sci.* **2020**, *10*, 2375.
https://doi.org/10.3390/app10072375

**AMA Style**

Harirchian E, Lahmer T. Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model. *Applied Sciences*. 2020; 10(7):2375.
https://doi.org/10.3390/app10072375

**Chicago/Turabian Style**

Harirchian, Ehsan, and Tom Lahmer. 2020. "Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model" *Applied Sciences* 10, no. 7: 2375.
https://doi.org/10.3390/app10072375