Development of Live Load Model for Strength II Limit State in AASHTO LRFD Design Specifications

Lou, Peng; Nassif, Hani; Truban, Paul

doi:10.1177/0361198118756874

Cited by 3 publications

(2 citation statements)

References 8 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Switzerland, passages of these transports cannot be regarded as rare occurrences as they occur on weekly or even daily basis. Similar situation is also in the USA as reported in [5]. This trend will increase in the future as the transportation industry is interested in using larger trucks with higher axle loads in order to improve their economies of scale.…”

Section: Challengessupporting

confidence: 69%

Existing Bridges – Burden or Opportunity?

Hajdin¹

2020

SJCE

View full text Add to dashboard Cite

It is widely accepted that safety and serviceability are primary concerns in bridge design. However, for the most of bridges' service life, these concerns are addressed indirectly by a qualitative measure, defined herein as condition state, which is based upon observable damages recorded during inspections. Condition state is at best, only loosely correlated to safety and serviceability. It would be more reasonable to address safety and serviceability in inspection process directly, using the information on bridge performance obtained during the design and construction. This seems inevitable given the ageing, deterioration, growing traffic and climate change. A vague measure for the deviation of inspected bridge from the "as new" condition i. e. condition state is simply not adequate tool to cope with these challenges. The future Bridge Management Systems (fBMS) should therefore include assessment of safety and serviceability based on inspection results and Structural Health Monitoring (SHM). This can be further enhanced by combining or merging BMS with Bridge Information Models that are currently being developed. The fBMS will thus become an invaluable decision-support tool not only for maintenance planning but also for specification of heavy vehicle corridors, risk assessment due to natural hazards, etc.

show abstract

Section: Challengessupporting

confidence: 69%

Existing Bridges – Burden or Opportunity?

Hajdin¹

2020

SJCE

View full text Add to dashboard Cite

show abstract

“…On the other hand, deterioration processes and changes in environmental conditions severely affect bridge structures’ safety and serviceability. Some examples are the reduction of resistance due to deterioration of concrete decks, corrosion or mechanical damage, the increasing traffic volume and vehicle weight [ 9 ], and the exposure to natural hazards due to climate change. Additionally, concrete material deterioration plays an essential role in the long-term damage accumulation and strength reduction in concrete structures [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Runtime Algorithm for the Real-Time Analysis and Detection of Unexpected Changes in a Real-Size SHM Network with Quasi-Distributed FBG Sensors

Sakiyama

Lehmann

Garrecht

2021

Sensors

View full text Add to dashboard Cite

The ability to track the structural condition of existing structures is one of the main concerns of bridge owners and operators. In the context of bridge maintenance programs, visual inspection predominates nowadays as the primary source of information. Yet, visual inspections alone are insufficient to satisfy the current needs for safety assessment. From this perspective, extensive research on structural health monitoring has been developed in recent decades. However, the transfer rate from laboratory experiments to real-case applications is still unsatisfactory. This paper addresses the main limitations that slow the deployment and the acceptance of real-size structural health monitoring systems (SHM) and presents a novel real-time analysis algorithm based on random variable correlation for condition monitoring. The proposed algorithm was designed to respond automatically to detect unexpected events, such as local structural failure, within a multitude of random dynamic loads. The results are part of a project on SHM, where a high sensor-count monitoring system based on long-gauge fiber Bragg grating sensors (LGFBG) was installed on a prestressed concrete bridge in Neckarsulm, Germany. The authors also present the data management system developed to handle a large amount of data, and demonstrate the results from one of the implemented post-processing methods, the principal component analysis (PCA). The results showed that the deployed SHM system successfully translates the massive raw data into meaningful information. The proposed real-time analysis algorithm delivers a reliable notification system that allows bridge managers to track unexpected events as a basis for decision-making.

show abstract

Prediction of Maximum Live-Load Effects for Bridges Based on Weigh-in-Motion Data

Lou,

Yang,

Nassif

2024

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Truck load spectra based on weigh-in-motion (WIM) measurements have been utilized in developing site-specific live load models to predict the maximum load effects on bridges. Conventional load extrapolation has been utilized to develop the AASHTO load-and-resistance factor design (LRFD) Bridge Design Specifications, while few studies have evaluated the accuracy of the load extrapolation techniques with actual data. The current AASHTO Manual for Bridge Evaluation (MBE) utilizes the top 5% of the load effects to extrapolate the 5-year maximum load effects for load rating. However, in the cases of high truck volume, the predicted 5-year maximum load effects using AASHTO MBE are significantly lower than the observed value because of the selection of the upper tail. Therefore, the choice of the upper tail size needs further validation. This paper proposes a modification to the conventional live load extrapolation method. Firstly, more accurate maximum load values for different return periods are determined through simulation and validated using 7 years of continuous data. Then, the values from conventional live load extrapolations using different upper tail sizes are obtained and compared with the simulation values. The optimal upper tail size is determined when the minimum error is yielded. The findings suggest that using a specific number of trucks for the upper tail yields greater accuracy compared to a percentage-based approach. Specifically, the recommended range is between 3,000 to 5,000 trucks, with an optimal number of 3,600. This paper concludes with recommendations to the AASHTO MBE to enhance the accuracy of live load extrapolation.

show abstract

Development of Live Load Model for Strength II Limit State in AASHTO LRFD Design Specifications

Cited by 3 publications

References 8 publications

Existing Bridges – Burden or Opportunity?

Existing Bridges – Burden or Opportunity?

A Novel Runtime Algorithm for the Real-Time Analysis and Detection of Unexpected Changes in a Real-Size SHM Network with Quasi-Distributed FBG Sensors

Prediction of Maximum Live-Load Effects for Bridges Based on Weigh-in-Motion Data

Contact Info

Product

Resources

About