Building damage assessment for deep excavations in Singapore and the influence of building stiffness

One of the biggest issues for underground construction in a densely built-up urban environment is the potentially adverse impact on buildings adjacent to deep excavations. In Singapore, a building damage assessment is usually carried out using a three-staged approach to assess the risk of damage caused by major underground construction projects. However, the tensile strains used for assessing the risk of building damage are often derived using deflection ratios and horizontal strains under 'greenfield' conditions. This ignores the effects of building stiffness and in many cases may be conservative. This paper presents some findings from a study on the response of buildings to deep excavations. Firstly, the paper discusses the settlement response of an actual building - the Singapore Art Museum - adjacent to a deep excavation. By comparing the monitored building settlement with the adjacent ground settlement markers, the influence of building stiffness in modifying the response to excavation-induced settlements is observed. Using the finite element method, a numerical study on the building response to movements induced by deep excavations found a consistent relationship between the building modification factor and a newly defined relative bending stiffness of the building. This relationship can be used as a design guidance to estimate the deflection ratio in a building from the greenfield condition. By comparing the case study results with the design guidance developed from finite element analysis, this paper presents some important characteristics of the influence of building stiffness on building damages for deep excavations.

Lateral spreading-induced abutment rotation in the 2011 Christchurch earthquake: Observations and analysis

A series of strong earthquakes near Christchurch, New Zealand, occurred between September 2010 and December 2011, causing widespread liquefaction throughout the city's suburbs. Lateral spreading developed along the city's Avon River, damaging many of the bridges east of the city centre. The short-to medium-span bridges exhibited a similar pattern of deformation, involving back-rotation of their abutments and compression of their decks. By explicitly considering the rotational equilibrium of the abutments about their point of contact with the rigid bridge decks, it is shown that relatively small kinematic demands from the laterally spreading backfill soil are needed to initiate pile yielding, and that this mode of deformation should be taken into account in the design of the abutments and abutment piles.

Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (∼10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance. © 2013 IEEE.

Avoiding pathologies in very deep networks

Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

Thermo-hydro-geomechanical simulations of methane gas production from deep sea methane hydrate formations

A new constitutive model called Methane Hydrate Critical State (MHCS) model was conducted to investigate the geomechanical response of the gas-hydrate-bearing sediments at the Nankai Trough during the wellbore construction process. The strength and dilatancy of gas-hydrate-bearing soil would gradually disappear when the bonds are destroyed because of excessively shearing, which are often observed in dense soils and also in bonded soils such as cemented soil and unsaturated soil. In this study, the MHCS model, which presents such softening features, would be incorporated into a staged-finite-element model in ABAQUS, which mainly considered the loading history of soils and the interaction between cement-casing-formation. This model shows the influence of gas-hydrate-bearing soil to the deformation and stability of a wellbore and the surrounding sediments during wellbore construction. At the same time, the conventional Mohr-Coulomb model was used in the model to show the advantages of MHCS model by comparing the results of the two models.

Fibre optic monitoring of a deep circular excavation

This paper describes part of the monitoring undertaken at Abbey Mills shaft F, one of the main shafts of Thames Water's Lee tunnel project in London, UK. This shaft, with an external diameter of 30 m and 73 m deep, is one of the largest ever constructed in the UK and consequently penetrates layered and challenging ground conditions (Terrace Gravel, London Clay, Lambeth Group, Thanet Sand Formation, Chalk Formation). Three out of the twenty 1-2 m thick and 84 m deep diaphragm wall panels were equipped with fibre optic instrumentation. Bending and circumferential hoop strains were measured using Brillouin optical time-domain reflectometry and analysis technologies. These measurements showed that the overall radial movement of the wall was very small. Prior to excavation during a dewatering trial, the shaft may have experienced three-dimensional deformation due to differential water pressures. During excavation, the measured hoop and bending strains of the wall in the chalk exceeded the predictions. This appears to be related to the verticality tolerances of the diaphragm wall and lower circumferential hoop stiffness of the diaphragm walls at deep depths. The findings from this case study provide valuable information for future deep shafts in London. © ICE Publishing: All rights reserved.