Abstract
Rapid Visual Screening (RVS) is a method of assessing a building’s vulnerability to an earthquake. Although RVS accurately classifies the damage states of the studied buildings, the parameter values assigned by this method have intrinsic uncertainties that can highly reduce the accuracy of its predictions. Accordingly, using a fuzzy logic model is known to mitigate these uncertainties and offer a more reliable level of damage prediction. A recently-proposed Dual-Fitness Particle Swarm Optimization (DFPSO) algorithm is applied to fine-tune the hyperparameters of this model, named the FLM-DFPSO model. Furthermore, the 1994 Northridge earthquake data is used to benchmark the performance of the proposed model against a well-known model in the literature. The results suggest that the proposed model improves the performance criteria by 7.46% and 27% in the training and validation stages, respectively. The model also boosts the prediction accuracy by a rate of 53% in the validation step, confirming the FLM-DFPSO as a highly reliable model to learn and also generalize the relationship between its inputs and output to other cases when assessing seismic damage.
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References
Allali SA, Abed M, Mebarki A (2018) Post-earthquake assessment of buildings damage using fuzzy logic. Engineering Structures 166:117–27, DOI: https://doi.org/10.1016/j.engstruct.2018.03.055
ATC-13 (1985) Earthquake damage evaluation data for california. Applied technology council. ATC-13 Report. Redwood City. California
ATC-38 (2001) Database on the performance of structures near strong-motion recordings: 1994 Northridge, California, Earthquake. Applied Technology Council, ATC-38 Report
Bektaş N, Kegyes-Brassai O (2022) Conventional RVS methods for seismic risk assessment for estimating the current situation of existing buildings: A State-of-the-Art Review. Sustainability 14(5):2583, DOI: https://doi.org/10.3390/su14052583
EERI (1994) Northridge earthquake, January 17, 1994; Preliminary reconnaissance report. Earthquake Engineering Research Institute. Oakland. California
Elms DG (2004) Structural safety issues and progress. Progress in Structural Engineering and Materials 6:116 126, DOI: https://doi.org/10.1002/pse.176
FEMA (1988) Rapid visual screening of buildings for potential seismic hazards: A handbook. Washington, DC, USA: Applied Technological Council (ATC)
FEMA (2002) Rapid visual screening of buildings for potential seismic hazards: A Handbook. Washington, DC, USA: Applied Technological Council (ATC)
FEMA (2015) Rapid visual screening of buildings for potential seismic hazards: A handbook. Applied Technological Council (ATC): Washington, DC, USA, DOI: https://doi.org/10.1061/9780784479728.064
Furuta H, Shiraishi N, Umano M, Kawakami K (1991) Knowledge-based expert system for damage assessment based on fuzzy reasoning. Computers & Structures 40(1):137–42, DOI: https://doi.org/10.1016/0045-7949(91)90466-Y
Gharehbaghi S, Gandomi M, Plevris V, Gandomi AH (2021) Prediction of seismic damage spectra using computational intelligence methods. Computers & Structures 253:106584
Ghobarah A (2000) Seismic assessment of existing RC structures. Progress in structural Engineering and Materials 2(1):60–71, DOI: https://doi.org/10.1002/(SICI)1528-2716(200001/03)2:1%3C60::AID-PSE8%3E3.0.CO;2-O
Harirchian E, Hosseini SE, Jadhav K, Kumari V, Rasulzade S, Işık E, Wasif M, Lahmer T (2021) A review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings. Journal of Building Engineering 43:102536, DOI: https://doi.org/10.1016/j.jobe.2021.102536
Harirchian E, Lahmer T (2020a) Developing a hierarchical type-2 fuzzy logic model to improve rapid evaluation of earthquake hazard safety of existing buildings. Structures 28:1384–1399, DOI: https://doi.org/10.1016/j.istruc.2020.09.048
Harirchian E, Lahmer T (2020b) Improved rapid visual earthquake hazard safety evaluation of existing buildings using a type-2 fuzzy logic model. Applied Sciences 10(7):2375, DOI: https://doi.org/10.3390/app10072375
Harirchian E, Lahmer T, Buddhiraju S, Mohammad K, Mosavi A (2020) Earthquake safety assessment of buildings through rapid visual screening. Buildings 10(3):51, DOI: https://doi.org/10.3390/buildings10030051
Haykin S (2009) Neural networks and learning machines. 3rd edn. Prentice Hall. Englewood Cliffs
Ketsap A, Hansapinyo C, Kronprasert N, Limkatanyu S (2019) Uncertainty and fuzzy decisions in earthquake risk evaluation of buildings. Engineering Journal 23(5):89–105, DOI: https://doi.org/10.4186/ej.2019.23.5.89
Miyasato GH, Dong W, Levitt RE, Boissonnade AC (1986) Implementation of a knowledge based seismic risk evaluation system on microcomputers. Artificial Intelligence in Engineering 1(1):29–35, DOI: https://doi.org/10.1016/0954-1810(86)90032-4
Nanda RP, Majhi DR (2013) Review on rapid seismic vulnerability assessment for bulk of buildings. Journal of The Institution of Engineers (India): Series A 94(3):187–97, DOI: https://doi.org/10.1007/s40030-013-0048-5
Norouzzadeh MS, Ahmadzadeh MR, Palhang M (2012) LADPSO: Using fuzzy logic to conduct PSO algorithm. Applied Intelligence 37:290–304, DOI: https://doi.org/10.1007/s10489-011-0328-6
Rezaei F, Safavi HR (2022) Sustainable conjunctive water use modeling using dual fitness particle swarm optimization algorithm. Water Resources Management 36:989–1006, DOI: https://doi.org/10.1007/s11269-022-03064-w
Rezaei F, Safavi HR, Mirchi A, Madani K (2017a) f-MOPSO: An alternative multi-objective PSO algorithm for conjunctive water use management. Journal of Hydro-environment Research 14:1–18, DOI: https://doi.org/10.1016/j.jher.2016.05.007
Rezaei F, Safavi HR, Zekri M (2017b) A hybrid fuzzy-based multi-objective PSO algorithm for conjunctive water use and optimal multi-crop pattern planning. Water Resour Manage 31(4):1139–1155, DOI: https://doi.org/10.1007/s11269-016-1567-4
Şen Z (2010) Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling. Expert Systems with Applications 37(8):5653–60, DOI: https://doi.org/10.1016/j.eswa.2010.02.046
Şen Z (2011) Supervised fuzzy logic modeling for building earthquake hazard assessment. Expert Systems with Applications 38(12):14564–73, DOI: https://doi.org/10.1016/j.eswa.2011.05.026
Shahriar A, Modirzadeh M, Sadiq R, Tesfamariam S (2012) Seismic induced damageability evaluation of steel buildings: A Fuzzy-TOPSIS method. Earthquake and Structures 3(5):695–717, DOI: https://doi.org/10.12989/eas.2012.3.5.695
Tesfamariam S (2008) Seismic risk assessment of reinforced concrete buildings using fuzzy based techniques. PhD Thesis. Department of Civil Engineering. University of Ottawa, DOI: https://doi.org/10.20381/ruor-19821
Tesfamariam S, Saatcioglu M (2008) Risk-based seismic evaluation of reinforced concrete buildings. Earthquake Spectra 24(3):795–821, DOI: https://doi.org/10.1193/1.2952767
Tesfamariam S, Saatcioglu M (2010) Seismic vulnerability assessment of reinforced concrete buildings using hierarchical fuzzy rule base modeling. Earthquake Spectra 26(1):235–56, DOI: https://doi.org/10.1193/1.3280115
Zhang D, Ma G, Deng Z, Wang Q, Zhang G, Zhou W (2022) A self-adaptive gradient-based particle swarm optimization algorithm with dynamic population topology. Applied Soft Computing 130:109660, DOI: https://doi.org/10.1016/j.asoc.2022.109660
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Zaribafian, O., Pourrostam, T., Fazilati, M. et al. Enhanced Accuracy of a Fuzzy Logic Model for Rapid Seismic Damage Prediction of RC Buildings. KSCE J Civ Eng 28, 250–261 (2024). https://doi.org/10.1007/s12205-023-2491-9
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DOI: https://doi.org/10.1007/s12205-023-2491-9