Centrifuge modelling and dynamic testing of municipal solid waste (MSW) landfills

An investigation into the seismic behaviour of municipal solid waste (MSW) landfills by dynamic centrifuge testing was undertaken. This paper presents physical modelling of MSW landfills for dynamic centrifuge testing, with regard to the following research areas: 1. amplification characteristics of municipal solid waste; 2. tension induced in geomembranes placed on landfill slopes due to earthquake loading; 3. damage to landfill liners due to liquefaction of foundation soil. A model waste, that has engineering properties similar to MSW, is presented. A model geomembrane that can be used in centrifuge tests is also presented. Results of dynamic centrifuge tests with the model geomembrane showed that an earthquake loading induces additional permanent tension (∼25%) in the geomembrane. © 2006 Taylor & Francis Group.

Centrifuge modelling and dynamic testing of municipal solid waste (MSW) landfills

An investigation into the seismic behaviour of municipal solidwaste (MSW) landfills by dynamic centrifuge testing was undertaken. This paper presents physical modelling of MSW landfills for dynamic centrifuge testing, with regard to the following research areas: 1. amplification characteristics of municipal solid waste; 2. tension induced in geomembranes placed on landfill slopes due to earthquake loading; 3. damage to landfill liners due to liquefaction of foundation soil. A model waste, that has engineering properties similar to MSW, is presented. A model geomembrane that can be used in centrifuge tests is also presented. Results of dynamic centrifuge tests with the model geomembrane showed that an earthquake loading induces additional permanent tension (∼25%) in the geomembrane. © 2006 Taylor & Francis Group, London.

Transcription of multi-genre media archives using out-of-domain data

We describe our work on developing a speech recognition system for multi-genre media archives. The high diversity of the data makes this a challenging recognition task, which may benefit from systems trained on a combination of in-domain and out-of-domain data. Working with tandem HMMs, we present Multi-level Adaptive Networks (MLAN), a novel technique for incorporating information from out-of-domain posterior features using deep neural networks. We show that it provides a substantial reduction in WER over other systems, with relative WER reductions of 15% over a PLP baseline, 9% over in-domain tandem features and 8% over the best out-of-domain tandem features. © 2012 IEEE.