Seismic risk for vernacular building classes in the fertile Indus Ganga alluvial plains at the foothills of the Himalayas, India

Low-cost housing initiative by the Government of India with the intention of providing housing for all is going to be effective only when sufficient understanding is developed about their ability to resist natural hazards and prevent disasters. The vernacular building classes, in the Indus Ganga basin, like Adobe with mud or lime and cement mortar, bricks with tiles or stone or metal sheet or concrete slab roof, are compared with reinforced concrete building standards, to assess the damage and loss estimates due to futuristic earthquake hazard at the foothills of the Himalayas

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