Special Survey Instrumentation for Deformation Measurements

Teskey, W. F.

doi:10.1061/(asce)0733-9453(1988)114:1(2)

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…The traditional foundation deformation monitoring method is a classic ground measurement method that uses traditional measuring instruments such as level, theodolite, and electronic total stations [9,10]. These traditional monitoring methods can only work when the train is stopped, and they consume manpower.…”

Section: Introductionmentioning

confidence: 99%

Differential Settlement of Track Foundations Identification Based on GRU Neural Network

et al. 2023

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The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations.

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Section: Introductionmentioning

confidence: 99%

Differential Settlement of Track Foundations Identification Based on GRU Neural Network

et al. 2023

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“…In order to provide strong basic data for any benchmark stability and deformation analysis, new technologies must be employed in order to integrate all types of measurements into comprehensive network of observables (Chrzanowski et al, 1986). There are short-term and long-term instabilities and deformations that occur to benchmarks, small or large structures and building should be investigated and reinvestigated time after time (Teskey, 1988b).…”

Section: Introductionmentioning

confidence: 99%

Determination of Benchmarks Stability within Ahmadu Bello University, Zaria, Niger

Ojigi¹

2011

FJOTE

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Heights of six geodetic benchmarks over a total distance of 8.6km at the Ahmadu Bello University (ABU), Zaria, Nigeria were recomputed and analysed using least squares adjustment technique. The network computations were tied to two fix primary reference pillars situated outside the campus. The two-tail Chi-square hypothesis test of the aposterior variances of the benchmark heights at 5% and 1% levels of significance were carried out in order to determine the reliability of the computed heights. This is meant to establish whether the observations used truly represent the heights of the Benchmarks in the study area. The results showed that, the estimated variances for fore, back and mean (of fore and back) leveling computations were 3.17mm, 1.77mm and 2.93mm respectively. The tests of these variances showed that the results, obtained from fore and mean leveling computations, do not truly represent the heights of the benchmarks. On the other hand, the back leveling computed variance passed the confidence limits test which tends to infer that the heights obtained significantly represent the heights of the benchmarks. That is, only the back leveling observation truly represent the benchmarks heights in the study area. Four out of the six benchmarks investigated for stability within the period of study showed displacement values ranging between 4.14mm and 12.42mm over a period of 17years. This may not be alarming but effort should be made toward the continuous monitoring of the benchmarks.

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“…1). Geotechnical and structural methods are the most accurate over short distances (Teskey, 1988). However, such methods only provide relative movement within the structures.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Analysis Procedure for Geometrical Detection of Spatial Deformation

Setan

1997

The Photogrammetric Record

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Methods for deformation monitoring are based on two-epoch analysis.During geometrical deformation detection, it is required to transform the results into a common datum, identify a set of stable points and localize the deformation. This paper describes a flexible analysis procedure that incorporates stability determination, congruency testing, S-transformations, localization and testing of spatial deformation. The procedure has been implemented into a computer program, DETECT. Two photogrammetric monitoring schemes were analysed using DETECT for identifying the significance of spatial deformation between epochs. The results confirmed the suitability of the procedure in practical applications.

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Special Survey Instrumentation for Deformation Measurements

Cited by 7 publications

References 3 publications

Differential Settlement of Track Foundations Identification Based on GRU Neural Network

Differential Settlement of Track Foundations Identification Based on GRU Neural Network

Determination of Benchmarks Stability within Ahmadu Bello University, Zaria, Niger

A Flexible Analysis Procedure for Geometrical Detection of Spatial Deformation

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