Stochastic response of reinforced concrete buildings using high dimensional model representation

Sahu, Deepak Kumar; Nishanth, M.; Dhir, Prateek Kumar; Sarkar, Pradip; Davis, Robin; Mangalathu, Sujith

doi:10.1016/j.engstruct.2018.10.083

Cited by 23 publications

(12 citation statements)

References 39 publications

(37 reference statements)

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“…Such approach is more convenient than the RSM when the number of inputs is particularly high. Although such method provides better accuracy than the RSM [22], an assumed functional form is needed also in this case for the adopted prediction function. Such methods have been adopted in earthquake engineering (e.g.…”

Section: Commonly Adopted Metamodeling Techniquesmentioning

confidence: 99%

Gaussian process regression for seismic fragility assessment of building portfolios

Gentile

Galasso

2020

Structural Safety

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Seismic fragility assessment of building portfolios is often based on the analysis of "average" building models representative of structural types (or building classes), thus neglecting building-to-building variability within a structural type. This paper proposes the use of gaussian process (GP) regressions to develop flexible and accurate metamodels explicitly mapping building-class attributes to the seismic fragility parameters. The proposed metamodels can enable analysts to account for building-to-building variability in simulation-based seismic risk assessment of building portfolios. Unlike other commonlyused metamodels, GP regressions do not require the a-priori definition of a prediction function and they quantify the uncertainty on the predictions in a refined and explicit fashion. The proposed method is demonstrated for a portfolio of seismicallydeficient reinforced concrete school buildings with construction details typical of some developing countries. Based on the available information about the building attributes (geometry, materials, detailing), building realisations are generated based on two alternative approaches, which are critically compared: design of experiment and Monte Carlo sampling. Cloud-based time-history analysis for each building realisation is performed using unscaled real ground-motion records; fragility relationships are derived for four structure-specific damage states. A GP regression is then developed for each considered fragility parameter (i.e. median and dispersion). To further increase the tractability of the methodology, alternative metamodels are defined based on numerical non-linear static pushover analyses or analytical "by-hand" pushover analyses, through the Simple Lateral Mechanism Analysis (SLaMA) method. The results show that, for the considered portfolio, the fitted GP regressions have a high predictive power in surrogating the modelled fragility, demonstrating the feasibility of the approach in practice. It is also shown that the choice of the sampling technique could be based on the input data availability, rather than on the expected computational burden. Finally, the use of simplified methods for response analysis shows acceptable error levels with respect to the full time-history analysis results. Such simplified methods can be promising alternatives to generate large training datasets for the proposed GP regressions. This increases the potential of training metamodels in practical portfolio risk assessment applications, in which a high number of building types, each characterised by a large number of attributes, is generally involved.

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Section: Commonly Adopted Metamodeling Techniquesmentioning

confidence: 99%

Gaussian process regression for seismic fragility assessment of building portfolios

Gentile

Galasso

2020

Structural Safety

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“…The SAC-FEMA method is formulated as a closed-form expression to analytically estimate the value of the risk integral convolving seismic hazard and structural response. This method is widely used by several researchers (Cornell et al 2002;Ramamoorthy et al 2006;Ellingwood et al 2007;Wu et al 2009;Ellingwood 2009, 2010;Davis et al 2010;Rajeev and Tesfamariam, 2012, Haran et al 2015Bhosale et al 2017Bhosale et al , 2018Dhir et al 2018;Sahu et al 2019) for the evaluation of seismic risks. An incremental dynamic analysis (IDA) framework introduced by previous researchers (Vamvatsikos and Cornell 2002;Dolsek 2009;Vamvatsikos and Fragiadakis 2010;Ferracuti et al 2009;Azarbakht and Dolšek 2011;Brunesi et al 2015;Kiani and Khanmohammadi 2015) is adopted in this study to develop the probabilistic seismic demand models (PSDMs) and fragility curves.…”

Section: Safety Assessment Methodologymentioning

confidence: 99%

Implications of Importance Factor on Seismic Design from 2000 SAC-FEMA Perspective

Dhir

Zade

Basu³

et al. 2020

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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The seismic design of buildings uses global ductility factor and occupancy importance factor (IF) as two major fixed parameters in defining the safety of the structure. The study of performance variation of the structure with global ductility factor is available but, there is hardly any study that provides information regarding the increase in the level of safety achieved by increasing the IF values. Being a building categorical dependent parameter, IF is used by the international seismic design codes for increasing the design loads of the structure. The change in the level of safety achieved through the variation in the value of the IFs for reinforced concrete (RC) framed buildings will perhaps be an important useful representation of the stakeholders for the approximate damage cost estimation. This article performs the structural safety assessment against seismic load using a standard structural reliability method with second-order hazard approximation to evaluate the effect of the IF on the level of safety and the cost associated with the building. Results show that, an overall reduction of 50-60% in the 3 damage index of the selected buildings can be achieved by increasing the IF from a value of 1.0 to 2.0 with a consequent increase in the cost of the building.

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“…Xie and DesRoches (2019) tested stepwise and LASSO regression models for probabilistic seismic demand analysis of California highway bridges. Other than RSMs, multi-predictor PSDMs have been developed using ANN (Calabrese and Lai, 2013; Lagaros et al, 2009; Lagaros and Fragiadakis, 2007; Liu and Zhang, 2018; Mitropoulou and Papadrakakis, 2011; Pang et al, 2014; Wang et al, 2018), bootstrapped ANN (Ferrario et al, 2017), SVM (Ghosh et al, 2018; Hariri-Ardebili and Pourkamali-Anaraki, 2018; Huang et al, 2017; Mahmoudi and Chouinard, 2016), kriging metamodeling (Gidaris et al, 2015), GLM (Xie et al, 2019a), MARS (Kameshwar and Padgett, 2014), K-nearest neighbor (Hariri-Ardebili and Pourkamali-Anaraki, 2018), naïve Bayes classifier (Hariri-Ardebili and Pourkamali-Anaraki, 2018), high-dimensional model representation (Sahu et al, 2019), and RF (Mangalathu and Jeon, 2019b).…”

Section: Seismic Fragility Assessmentmentioning

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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Stochastic response of reinforced concrete buildings using high dimensional model representation

Cited by 23 publications

References 39 publications

Gaussian process regression for seismic fragility assessment of building portfolios

Gaussian process regression for seismic fragility assessment of building portfolios

Implications of Importance Factor on Seismic Design from 2000 SAC-FEMA Perspective

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

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