Probabilistic Modeling of Single and Concurrent Truckloads on Bridges

2017 ◽  
Vol 2609 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Donald C. Watson ◽  
Matthew Crim ◽  
Kurtis R. Gurley ◽  
Scott S. Washburn
Keyword(s):  
Minimum Weight ◽  
Probabilistic Modeling ◽  
Rating System ◽  
Maintenance Planning ◽  
Modeling Approach ◽  
Heavy Trucks ◽  
Bridge Maintenance ◽  
Weigh In Motion ◽  
Probabilistic Loading ◽  
Bridge Rating

The maintenance of bridges and the evolution of an appropriate bridge rating system require the consideration of loads from heavy trucks. These loads can arise from a single overweight truck or multiple trucks simultaneously present, or concurrent, on a bridge. This paper presents a probabilistic modeling approach to assess the frequency and likelihood of observing various bridge loads caused by single and concurrent trucks. The approach used weigh-in-motion (WIM) data collected at or near bridges of interest to identify single and concurrent trucks and their Corresponding loads. The modeling approach was applied to bridges near three WIM stations in Florida. Results showed that in any given month, there was a 100% probability of observing at least one single or concurrent truckload that exceeded twice the minimum weight of a single overweight truck (i.e., exceeded 711,715 N or 160,000 lb). In addition, the probability of observing extreme truckloads was significantly higher when all trucks were considered, as opposed to only overweight trucks. The modeling approach can easily be adapted to the goals of the study and to any region where WIM data are available at or near the bridge(s) of interest. Results generated from the modeling approach provide probabilistic loading input for bridge maintenance planning and truck permitting policy.

Kernel Distributions in Main Spikes of Salt‐Stressed Wheat: A Probabilistic Modeling Approach

Crop Science ◽  
1992 ◽  
Vol 32 (3) ◽  
pp. 704-712 ◽  
Author(s):  
Scott M. Lesch ◽  
Catherine M. Grieve ◽  
Eugene V. Maas ◽  
Leland E. Francois
Keyword(s):  
Probabilistic Modeling ◽  
Modeling Approach

scholarly journals A Probabilistic Approach for Economic Evaluation of Occupational Health and Safety Interventions: A Case Study of Silica Exposure Reduction Interventions in the Construction Sector

2020 ◽  
Author(s):  
Amirabbas Mofidi ◽  
Emile Tompa ◽  
SeyedBagher Mortazavi ◽  
Akbar Esfahanipour ◽  
Paul A. Demers
Keyword(s):  
Lung Cancer ◽  
Economic Evaluation ◽  
Probabilistic Modeling ◽  
Hazardous Substances ◽  
Construction Sector ◽  
Net Benefit ◽  
Expected Cost ◽  
Silica Exposure ◽  
Exposure Reduction ◽  
Modeling Approach

Abstract Background: Construction workers are at a high risk of exposure to various types of hazardous substances such as crystalline silica. Though multiple studies indicate the evidence regarding the effectiveness of different silica exposure reduction interventions in the construction sector, the decisions for selecting a specific silica exposure reduction intervention are best informed by an economic evaluation. Economic evaluation of interventions is subjected to uncertainties in practice, mostly due to the lack of precise data on important variables. In this study, we aim to identify the most cost-beneficial silica exposure reduction intervention for the construction sector under uncertain situation. Methods: We apply a probabilistic modeling approach that covers a large number of variables relevant to the cost of lung cancer, as well as the costs of silica exposure reduction interventions. To estimate the societal lifetime cost of lung cancer, we use an incidence cost approach. To estimate the net benefit of each intervention, we compare the expected cost of lung cancer cases averted, with expected cost of implementation of the intervention in one calendar year. Sensitivity analysis is used to quantify how different variables effects interventions net benefit.Results: A positive net benefit is expected for all considered interventions. The highest number of lung cancer cases are averted by combined use of wet method, local exhaust ventilation and personal protective equipment, about 107 cases, with expected net benefit of $45.9 million. Results also suggest that the level of exposure is an important determinant for the selection of the most cost-beneficial intervention.Conclusions: This study provides important insights for decision makers about silica exposure reduction interventions in the construction sector. It also provides an overview of the potential advantages of using probabilistic modeling approach to undertake economic evaluations, particularly when researchers are confronted with a large number of uncertain variables.

Risk-based Multiobjective Optimization Model for Bridge Maintenance Planning

2010 ◽  
Author(s):  
I-Tung Yang ◽  
Yen-Shun Hsu ◽  
Jane W. Z. Lu ◽  
Andrew Y. T. Leung ◽  
Vai Pan Iu ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Model ◽  
Maintenance Planning ◽  
Bridge Maintenance

SHM-based F-AHP bridge rating system with application to Tsing Ma Bridge

2011 ◽  
Vol 5 (4) ◽  
pp. 465-478 ◽  
Author(s):  
Qi Li ◽  
You-lin Xu ◽  
Yue Zheng ◽  
An-xin Guo ◽  
Kai-yuen Wong ◽  
...  
Keyword(s):  
Rating System ◽  
Tsing Ma Bridge ◽  
Bridge Rating

A probabilistic modeling approach for interpretable data inference and classification

2020 ◽  
pp. 1-17
Author(s):  
Shuaiyu Yao ◽  
Jian-Bo Yang ◽  
Dong-Ling Xu
Keyword(s):  
Statistical Analysis ◽  
Probabilistic Modeling ◽  
Data Set ◽  
Inference Mechanism ◽  
Modeling Approach ◽  
Input Variables ◽  
Data Inference ◽  
The Relationship ◽  
Better Than ◽  
Iris Data

In this paper, we propose a new probabilistic modeling approach for interpretable inference and classification using the maximum likelihood evidential reasoning (MAKER) framework. This approach integrates statistical analysis, hybrid evidence combination and belief rule-based (BRB) inference, and machine learning. Statistical analysis is used to acquire evidence from data. The BRB inference is applied to analyze the relationship between system inputs and outputs. An interdependence index is used to quantify the interdependence between input variables. An adapted genetic algorithm is applied to train the models. The model established by the approach features a unique strong interpretability, which is reflected in three aspects: (1) interpretable evidence acquisition, (2) interpretable inference mechanism, and (3) interpretable parameters determination. The MAKER-based model is shown to be a competitive classifier for the Banana, Haberman’s survival, and Iris data set, and generally performs better than other interpretable classifiers, e.g., complex tree, logistic regression, and naive Bayes.

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