Uncertainty Quantification of Structural Response Due to Earthquake Loading

In the performance-based structural design, the crucial component is to estimate the uncertainties in the structural responses precisely. The uncertainty may lie in many structural design parameters such as load, material properties, etc. Each parametric uncertainty has led to variation in the structural responses. Currently, structural design is performed considering the constant loading and material properties. But in reality, all these parameters are highly uncertain and can pose a wide variation in the structural responses due to earthquake loading. This study focuses on identifying the uncertainties arising from the different means and their impacts on the responses. The Monte Carlo Sampling (MCS) is employed to quantify the uncertainties in a structural deformation. The Multi-Degrees of Freedom (MDOF) structural model is constructed in the OpenSees program, and non-linear dynamic analysis is performed. The El-Centro earthquake was applied for the structural analysis. The result shows the probabilistic distribution of the earthquake response parameters. This approach is a more realistic representation of structural responses by incorporating the uncertainties in the design parameters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

Single-Component/Single-Station–Based Machine Learning for Estimating Magnitude and Location of an Earthquake: A Support Vector Machine Approach

Traditional approaches require a velocity model to compute travel times for estimating the location of earthquakes. Moreover, the velocity model typically assumed is layered in nature, ignoring the perturbations around the background velocity model. In this study, we propose a novel method that does not require any assumption on a velocity model. Also, triangulation estimates of location require three-station recordings. Even if the single station is used, three components of recordings are needed. In this work, we propose a machine learning method, based on support vector machines (SVM), that can predict earthquake location and magnitude with just one component (vertical) of a single station. Machine learning methods are typically used for either regression or classification. Literature shows that the SVM algorithm is promising for the classification of an arbitrary signal. This research demonstrates that, by using complementary input features from a seismic recording, a comparable performance can be obtained, with a substantial reduction in detection time. Furthermore, only the vertical component of a single recording station data is used to train the network. The SVM algorithm is applied on synthetic seismograms from 400 earthquakes of different focal mechanisms and magnitudes. This falls under supervised machine learning, as we give the input features and use them for training the model. Overfitting is avoided by tenfold cross-validation, i.e., the algorithm is repeated ten times, each time giving a different 10% as a testing portion. The classifier's performance is quite good, on the synthetic noise-free data, in predicting the magnitude, elevation angle, and hypocentral distance, but not the azimuthal angle. The trained model in the present work can be readily used where noise levels are quite low without sophisticated ray-tracing or Green's function computation. The performance of the algorithm is checked by adding additive noise to the data. The novelty of this work is that knowledge of the velocity model is not required, and the estimation of magnitude and the origin of the earthquake are done using the single-station/single-component earthquake records

Machine learning algorithms applied to engineering seismology and earthquake engineering

Machine learning algorithms are used in this thesis to predict earthquake parameters for simulated and recorded events. First, the seismological engineering problem of location and magnitude is addressed using machine learning algorithms on synthetic seismograms. Only a single station/ Single component seismogram is used to estimate the earthquake location and the magnitude. The data-driven machine learning techniques are useful to extract the information from the seismograms. The Ground Motion Prediction Equations (GMPEs) like machine learning models have been developed for the different seismically active regions. Ground motion parameters such as intensity, peak ground motion, and earthquake duration are addressed using recorded data for various tectonic regimes worldwide. Ensemble learning algorithms based on majority voting have also been tried out in the thesis and for each seismically active region considered, the best performing algorithm is identified

Explainable Machine learning on New Zealand strong motion for PGV and PGA

Estimating ground motion characteristics at various locations as a function of fault characteristics is useful for the proper damage assessment and risk mitigation strategies. This paper explores the application of machine learning approaches to predict peak ground acceleration (PGA) and peak ground velocity (PGV) using New Zealand's strong motion data. Five machine learning algorithms, namely linear regression, kNN, SVM, Random Forest, and XGBoost, are used in this study. Using the New Zealand flat-file database, the geometric mean of the peak ground motion parameters is used as predictor variables in training the machine learning algorithms. The performance of the chosen algorithms and how they work on PGV and PGA are discussed. The best prediction for PGA is obtained using random forest but for PGV XGboost worked best. The relative importance of various features in the flat file is also presented for the best-performing machine learning algorithm. Although the magnitude of an earthquake is found to be most influential for PGV, rupture distance showed the highest impact for PGA. Finally, the predictions are also explained using SHApley Additive exPlanations (SHAP) for the overall dataset as well as on a sample by sample basis, for a few samples. Pairwise dependency of some features with the highest feature importance is also presented using SHAP. © 2021 Institution of Structural Engineer

Spectral acceleration prediction for strike, dip, and rake: a multi-layered perceptron approach

A multi-layer perceptron (MLP) technique is used to train on the response spectra for various strike angles, dip angles, and rake angles. Fixing the magnitude and depth of the earthquakes, the 3-component ground motion is simulated with the help of SPECFEM3D. The residuals of spectral acceleration as a function of time period, for low-rise to high-rise structures, are found to be free of any trend. The hidden layers in the MLP learn the interdependency of focal mechanism parameters on the response spectrum. The resultant model was checked for attenuation characteristics with respect to distance. Furthermore, the trained MLP also showed a shift in spectral peak due to radiation damping, as expected. This MLP architecture presented in this work can be broadly extended to predict the response spectrum, at bedrock level, for any focal mechanism parameters, i.e., strike, dip, and rake, depending on the velocity model of that region

Duration prediction of Chilean strong motion data using machine learning

Chile is rocked by inslab, interface as well as crustal events. Duration estimates based on Chilean strong motion flatfile is used to predict total duration as well as significant-duration. We use six different machine learning algorithms k-nearest neighbours, support vector machine, Random forest, Neural network, AdaBoost, decision tree and estimate the accuracies of prediction for each component (EW, NS, Z) of ground motion for different tectonic environments. The estimates of duration using machine learning are found to be quite accurate and the best performing machine learning algorithm in prediction of the total duration and the significant-duration are highlighted

Estimation of tuberculosis incidence at subnational level using three methods to monitor progress towards ending TB in India, 2015–2020

Objectives We verified subnational (state/union territory (UT)/district) claims of achievements in reducing tuberculosis (TB) incidence in 2020 compared with 2015, in India.Design A community-based survey, analysis of programme data and anti-TB drug sales and utilisation data.Setting National TB Elimination Program and private TB treatment settings in 73 districts that had filed a claim to the Central TB Division of India for progress towards TB-free status.Participants Each district was divided into survey units (SU) and one village/ward was randomly selected from each SU. All household members in the selected village were interviewed. Sputum from participants with a history of anti-TB therapy (ATT), those currently experiencing chest symptoms or on ATT were tested using Xpert/Rif/TrueNat. The survey continued until 30 Mycobacterium tuberculosis cases were identified in a district.Outcome measures We calculated a direct estimate of TB incidence based on incident cases identified in the survey. We calculated an under-reporting factor by matching these cases within the TB notification system. The TB notification adjusted for this factor was the estimate by the indirect method. We also calculated TB incidence from drug sale data in the private sector and drug utilisation data in the public sector. We compared the three estimates of TB incidence in 2020 with TB incidence in 2015.Results The estimated direct incidence ranged from 19 (Purba Medinipur, West Bengal) to 1457 (Jaintia Hills, Meghalaya) per 100 000 population. Indirect estimates of incidence ranged between 19 (Diu, Dadra and Nagar Haveli) and 788 (Dumka, Jharkhand) per 100 000 population. The incidence using drug sale data ranged from 19 per 100 000 population in Diu, Dadra and Nagar Haveli to 651 per 100 000 population in Centenary, Maharashtra.Conclusion TB incidence in 1 state, 2 UTs and 35 districts had declined by at least 20% since 2015. Two districts in India were declared TB free in 2020