English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Clustering analysis of sequence data continues to address many applicationsin engineering design, aided with the rapid growth of machine learning inapplied science. This paper presents an unsupervised machine learning algorithmto extract defining characteristics of earthquake ground-motion spectra, alsocalled latent features, to aid in ground-motion selection (GMS). In thiscontext, a latent feature is a low-dimensional machine-discovered spectralcharacteristic learned through nonlinear relationships of a neural networkautoencoder. Machine discovered latent features can be combined withtraditionally defined intensity measures and clustering can be performed toselect a representative subgroup from a large ground-motion suite. Theobjective of efficient GMS is to choose characteristic records representativeof what the structure will probabilistically experience in its lifetime. Threeexamples are presented to validate this approach, including the use ofsynthetic and field recorded ground-motion datasets. The presented deepembedding clustering of ground-motion spectra has three main advantages: 1.defining characteristics the represent the sparse spectral content ofground-motions are discovered efficiently through training of the autoencoder,2. domain knowledge is incorporated into the machine learning framework withconditional variables in the deep embedding scheme, and 3. method exhibitsexcellent performance when compared to a benchmark seismic hazard analysis.

An Unsupervised Machine Learning Approach for Ground-Motion Spectra Clustering and Selection