Developments in the Use of Neural Nets for Truck Weigh-in-Motion on Steel Bridges

2009 ◽  
Author(s):  
I. Flood ◽  
R.R.A. Issa ◽  
N. Kartam
Keyword(s):  
Steel Bridges ◽  
Neural Nets ◽  
Weigh In Motion

Reliability Assessment of Steel Bridges for Specialized Hauling Vehicles

2019 ◽  
Vol 2673 (12) ◽  
pp. 391-403
Author(s):  
Peng Lou ◽  
Dongjian Gao ◽  
Hani Nassif ◽  
Mula Reddy
Keyword(s):  
Safety Margin ◽  
Steel Bridges ◽  
Live Load ◽  
Load Rating ◽  
Truck Traffic ◽  
Reliability Indices ◽  
Weigh In Motion ◽  
Load Factors ◽  
Load Effects ◽  
Existing Bridges

Specialized hauling vehicles (SHVs) are short heavy trucks within the legal weight limits but induce higher load effects than routine commercial loads. The Manual for Bridge Evaluation (MBE) adopted a series of single-unit trucks (SUs) to represent this type of vehicle. However, the SUs were introduced without rigorous reliability-based analysis due to the lack of data on SHVs. With the availability of vast amounts of data on weigh-in-motion (WIM) truck weights and configurations, the reliability of steel bridges under the SHVs should be evaluated in a more robust and quantitative manner. Through the utilization of WIM data, the authors quantified the SHVs in terms of percentages of SHVs among all truck traffic, daily average counts of SHVs, and number of axles. The gross vehicle weights (GVWs) and typical configurations of SHVs were investigated. In addition, their load effects were determined and normalized by the corresponding SUs. The maximum live loads corresponding to a return period of 5 years were also extrapolated using normal probability paper (NPP). To evaluate the effectiveness of current load-rating procedures for SHVs, the authors investigated the relationship between the load-rating factors and the corresponding reliability indices for existing bridges using the developed live load parameters based on the WIM data. Results indicated that the current live load factors were not able to provide a uniform and appropriate reliability index at different average daily truck traffic (ADTT) scenarios. This paper thus proposes new live load factors and weight adjustments of SU trucks to provide an adequate and uniform safety margin for the evaluation of steel bridges.

Steel Bridges – Solutions for the 21st Century

2002 ◽  
Vol 86 (16) ◽  
pp. 125-133
Author(s):  
Frank Rapattoni
Keyword(s):  
21St Century ◽  
Steel Bridges

An Examination on Hybrid Structures Renovated from the Old Railway Steel Bridges with Polymer Cement Mortar

2016 ◽  
Vol 106 (1) ◽  
pp. 1014-1019
Author(s):  
Nozomu TANIGUCHI ◽  
Yusuke SUGINO ◽  
Fujikazu OHKUBO ◽  
Weiwei LIN ◽  
Shinya SATAKE ◽  
...  
Keyword(s):  
Cement Mortar ◽  
Steel Bridges ◽  
Hybrid Structures

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

Neural Nets for Scene Analysis

1992 ◽  
Author(s):  
David Casasent
Keyword(s):  
Neural Nets ◽  
Scene Analysis
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