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Record W2993338881

Dynamic bridge-vehicle interactions

2014· article· en· W2993338881 on OpenAlex
Torill Pape, Rudolph Kotzé, Hanson Ngo, R Pritchard, W. Milnor Roberts, Tierang Liu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueRoad and transport research · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)EngineeringTransport engineeringAllowance (engineering)Load factorComputer scienceStructural engineeringOperations management
DOInot available

Abstract

fetched live from OpenAlex

The interaction between vehicles and bridges remains a complex yet important concept in the assessment of dynamic loading on existing structures. The dynamic impact of vehicular loading on a structure is typically accounted for in the assessment procedure by the application of a dynamic load allowance (DLA) factor to the assessment load, with a factor of 0.4 specified in the Australian bridge design code AS 5100. This factor is historically based on empirical dynamic load test data that underpins the Canadian bridge design codes. The Queensland Department of Transport and Main Roads (TMR) has adopted the AS 5100 DLA factor in its base level, Tier 1 Bridge Heavy Load Assessment Brief (2013). However it is looking to develop a better understanding of a family of bridges for higher-order bridge assessments when adopting dynamic load factors, taking into account various vehicle and structure types and dynamic influences. This in turn may lead to a review of vehicle access and ensure efficient use of resources. To address these issues and to improve understanding on bridge-vehicle interactions, TMR has initiated a three-year research program in conjunction with ARRB Group. This paper presents the findings from the first year of the program. In summary, a detailed literature review has yielded valuable information pertaining to various factors influencing the assessment of bridge-vehicle dynamic interactions and the background to the adoption of the current DLA factors. A gap analysis has shown that very little practical information has been published regarding the dynamic impact of hydro-pneumatic cranes and road trains on bridges. A significant review of previous load test reports from national and international jurisdictions revealed that various structure types, vehicle types, and materials can influence the dynamic response of a structure to dynamic loads. Frequency matching between vehicles and structures can also result in significant load amplification. A recent load test on Canal Creek Bridge in Cloncurry, Queensland, using various vehicle types and suspensions supports these observations. Finally, a review of the viability and applicability of the development of a Vehicle-Bridge Interaction model for TMR use has been conducted. Additional field tests are scheduled as part of the program, with validation and calibration of models and experimental findings and recommendations to be completed in the final year.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.308
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it