Neuro-fuzzy techniques for the classification of earthquake damages in buildings

Alvanitopoulos, Petros-Fotios; Andreadis, Ioannis; Elenas, Anaxagoras

doi:10.1016/j.measurement.2010.02.011

Cited by 30 publications

(11 citation statements)

References 11 publications

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“…ANNs can be coupled with fuzzy logic algorithms to take advantage of the computational capabilities from both algorithms in a complimentary way. For instance, imprecise data information, such as linguistic statements, can be transformed into numerical data through a fuzzy logic interface for an ANN to train and make decisions (Alvanitopoulos et al, 2010). GAs can interact with an ANN in various steps to improve its performance (Adeli, 2001).…”

Section: Methods Used In Earthquake Engineeringmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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Section: Methods Used In Earthquake Engineeringmentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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“…Ibrahim et al [42] used a dataadaptive NF inference model for early detection and classification of diabetes disease based on symptoms. Alvanitopoulos et al [43] proposed a NF classification technique for the identification of damages produced by an earthquake on construction. After the manifestation of the earthquake, the evaluation of the safety of existing structure and measure to be taken for automatic damage classification of buildings is considered.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Das

Naik

Behera

2020

Advances in Fuzzy Systems

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The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During the fuzzification process, all the features are expanded based on the number of classes available in the dataset. It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. However, the complexity of the problem increases due to this expansion of input features in the fuzzification process. These expanded features may not always contribute significantly to the model. To overcome this problem, feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. These reduced significant features are used in the ANN-based model to classify the data. The effectiveness of this proposed model is tested and validated with ten benchmark datasets (both balanced and unbalanced) to demonstrate the performance of the proposed NF-FR model. The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information.

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“…The study not only showed that DBS is more effective than levodopa but it did it in a way that is less time-consuming and less expensive than MRI and other medical expert dependant solutions. In another case ANN and Neuro-Fuzzy based models were applied to classify earthquake damages in buildings which can be utilized to help engineers decide whether some structures are remained safe or not [22]. The presented classification models used 20 seismic parameters of accelerograms to estimate 4 damage categories.…”

Section: Wali Et Al Compared Intelligent Controllers Such Asmentioning

confidence: 99%

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Viharos

Kis

2015

Measurement

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Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use inputoutput patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a NeuroFuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement.

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Neuro-fuzzy techniques for the classification of earthquake damages in buildings

Cited by 30 publications

References 11 publications

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

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