Utilising artificial neural networks for assessing seismic demands of buckling restrained braces due to pulse-like motions

Buckling restrained brace frames (BRBFs) exhibit exceptional lateral stiffness, load-bearing capacity, and energy dissipation properties, rendering them a highly promising choice for regions susceptible to seismic activity. The precise and expeditious prediction of seismic demands on BRBFs is a crucial and challenging task. In this paper, the potential of artificial neural networks (ANNs) to predict the seismic demands of BRBFs is explored. The study presents the characteristics and modelling of prototype BRBFs with different numbers of stories and material properties, utilising the OpenSees software (Version 2.5.0) for numerical simulations. The seismic performance of the BRBFs is evaluated using 91 near-fault pulse-like ground motions, and the maximum inter-storey drift ratio (MIDR) and global drift ratio (GDR) are recorded as a measure of seismic demand. ANNs are then trained to predict the MIDR and GDR of the selected prototypes. The model’s performance is assessed by analysing the residuals and error metrics and then comparing the trend of the results with the real dataset. Feature selection is utilised to decrease the complexity of the problem, with spectral acceleration at the fundamental period (T) of the structure (Sa), peak ground acceleration (PGA), peak ground velocity (PGV), and T being the primary factors impacting seismic demand estimation. The findings demonstrate the effectiveness of the proposed ANN approach in accurately predicting the seismic demands of BRBFs.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020

A new model for the prediction of earthquake ground-motion duration in Iran

The paper proposes a new empirical model to estimate earthquake ground-motion duration, which significantly influences the damage potential of an earthquake. The paper is concerned with significant duration parameters that are defined as the time intervals between which specified values of Arias intensity are reached. In the proposed model, significant duration parameters have been expressed as a function of moment magnitude, closest site-source distance, and site condition. The predictive model has been developed based on a database of earthquake ground-motion records in Iran, containing 286 records up to the year 2007, and a random-effect regression procedure. The result of the proposed model has been compared with that of other published models. It has been found that the proposed model can predict earthquake ground-motion duration in Iran with adequate accuracy

Site class mapping based on earthquake ground motion data recorded by regional seismographic network

This paper presents a methodology for site class mapping in regions without adequate geotechnical, geologic and geomorphologic data, which is prevailing in many less developed regions globally. The proposed methodology is based primarily on analysis of earthquake ground motion data recorded by regional seismographic network. Three analysis methods independently developed have been adopted with appropriate weightings, from which a continuous value of site class index ranging from 1 to 4 could be assigned to each station. Finally, a regional site class map could be developed by applying an interpolation procedure across all stations of the seismographic network of which the site classes were estimated

A new site classification approach based on neural networks

Site classification is an important procedure for a reliable site-specific seismic hazard assessment. On the other hand, the site conditions at strong-motion stations are essential for accurate interpretation and analysis of the recorded ground motion data obtained from different regions of the world. For some countries with insufficient data on the subsurface geological settings, the required site condition information is not available. This paper presents a new and efficient approach for site classification based on artificial neural networks (ANN) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves for four site classes. The nonlinear nature of ANN and their ability to learn in a complex environment make it highly suitable for function approximation and solving complicated engineering problems. Two types of radial basis function (RBF) neural networks, namely, probabilistic neural networks (PNN) and generalized regression neural networks (GRNN) were chosen in this study, as no separate training phase is required, rendering them particularly suitable for site classification. The proposed approach has been tested using data of the Chi-Chi, Taiwan, earthquake (Mw=7.6) recorded from 87 stations at which the site conditions are known. Analyses show that both the PNN and the GRNN perform very well with similar accuracy in estimating site conditions, with successful rates of 78% and 75%, respectively

Evaluation of damping modification factors for seismic response spectra

Seismic response spectra with structural damping ratio other than nominal 5% (of critical damping) are essential for the design and evaluation of structures in performance-based seismic engineering. Such response spectra are also essential for the design and evaluation of structures with seismic isolation and energy dissipation systems. A number of formulations for damping modification factors (DMF) have been proposed in the literature for scaling the 5% damped response spectra. Dependence of the DMF on several ground motion parameters has also been identified. Few seismic design codes have already incorporated simplified DMF based on these studies. This paper critically reviews the available formulations for DMF for seismic response spectra. Analytical investigations on the ground motion response spectra at soil sites, based on a wide range of simulated ground motion records, have been carried out. It has been observed that the DMF for ground motion response spectra at soil sites is significantly dependent on site period, which has not been identified in previous studies. The influences of earthquake shaking level, earthquake source-site distance (nearfield and far-field events), soil plasticity index, and the rigidity of bedrock have also been investigated

Correction to: A new model for the prediction of earthquake ground-motion duration in Iran (Natural Hazards, (2014), 70, 1, (69-92), 10.1007/s11069-011-9990-6)

In the originally published version of this manuscript published in Natural Hazards (2014) 70:69–92 from Yaghmaei-Sabegh et al., there was a typo in Eq. 5 where the (Formula presented.) coefficient was not typed, thus the revised and correct form is stated as follows: (Formula presented.

Conversion between peak ground motion parameters and modified mercalli intensity values

A model developed recently for conversion of instrumental data to Modified Mercalli Intensity data in North America has been evaluated in this study which employed strong motion recordings and matching Intensity data from ten Iranian earthquakes. Iranian data has also been used to develop new expressions for estimation of Intensity values for various peak ground motion parameters. Importantly, predictions from the new expression with peak ground velocity as the predictor is consistent with those from well-published models developed from data collected in North America. The demonstrated generality of existing conversion relationships has important implications to damage assessment across the globe