Fatigue Performance of Piezoelectric Weigh-in-Motion Sensors

Author(s):  
A. T. Papagiannakis ◽  
E. C. Johnston ◽  
S. Alavi
2001 ◽  
Vol 29 (6) ◽  
pp. 535 ◽  
Author(s):  
RE Petersen ◽  
RE Link ◽  
AT Papagiannakis ◽  
EC Johnston ◽  
S Alavi ◽  
...  

1998 ◽  
Author(s):  
Rajesh K. Panda ◽  
Patrick J. Szary ◽  
Ali Maher ◽  
Ahmad Safari

2018 ◽  
Vol 18 (3) ◽  
pp. 934-948 ◽  
Author(s):  
Yang Deng ◽  
Aiqun Li ◽  
Dongming Feng

Hangers or suspenders of a suspension bridge are the primary load-carrying members and are vital to the structural integrity and service life of the bridge. Site-specific vehicle loads monitored by the weigh-in-motion system can assist to obtain the operational cyclic stresses of hangers. Differing from most existing studies, herein, a framework for fatigue performance investigation for hangers of suspension bridges is proposed utilizing the full information of the weigh-in-motion data. This framework includes four steps: (1) generate influence surfaces for hangers, (2) reconstruct vehicular loading flows based on the weigh-in-motion data, (3) calculate time histories of hanger tension forces, and (4) evaluate fatigue damages and predict fatigue lives. Critical issues, such as the loading configuration of trucks, the threshold of the gross vehicle weight, and the time step for stress calculation, have been studied and discussed in detail. Based on 8-month weigh-in-motion data of a prototype suspension bridge, it is shown that the fatigue damage of hangers can be evaluated day by day, and subsequently the fatigue lives can be predicted. The correlation between the fatigue damages and vehicular loads is also investigated in this study.


Author(s):  
Fatemeh Sayyady ◽  
Yahya Fathi ◽  
George F. List ◽  
John R. Stone

This paper considers the problem of finding optimal sensor locations on a traffic network with the goal of characterizing system use overall. The problem is studied for two practical scenarios. In the first scenario, it is assumed that there is a given number of sensors (p) to be located on the highway network. In this context, the problem is to find a collection of p locations among a given collection of candidate locations. In the second scenario, it is assumed that there is a cost (ci) associated with installing a sensor at each candidate location i and a total budget b. In this context, the problem is to find a collection of locations that provide the best possible characterization given the budget constraint. A metric is proposed for evaluating a potential solution, and then appropriate mathematical models are proposed for solving the problem for each scenario. It is shown that the budget-constrained problem is an extension of the well-known p-median problem. A new Lagrangian heuristic algorithm is presented for solving large instances of this problem when a budget constraint is imposed. A comprehensive computational experiment is used to demonstrate that the Lagrangian heuristic algorithm provides solutions for large-scale networks within reasonable execution times. Examples are based on locating weigh-in-motion sensors on a large-scale highway network.


Author(s):  
Ronald White ◽  
Jongchul Song ◽  
Carl Haas ◽  
Dan Middleton

2013 ◽  
Vol 139 (9) ◽  
pp. 913-922
Author(s):  
Shahram Hashemi Vaziri ◽  
Carl T. Haas ◽  
Leo Rothenburg ◽  
Ralph C. Haas

2001 ◽  
Vol 1769 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Sirous H. Alavi ◽  
Joseph A. Mactutis ◽  
Scott D. Gibson ◽  
A. Thomas Papagiannakis ◽  
David Reynaud

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