2020Journal of Bridge EngineeringRequires access

Revisited Live Load for Simple-Span Bridges

Olga Iatsko, Andrzej S. Nowak

Open publisher page 16 citations

Abstract

Twenty-five years ago, a bridge live-load model was developed along with the calibration of the AASHTO LRFD Specifications. The statistical parameters of truck loads were based on the Ontario, Canada, truck survey performed in the 1970s. The objective of this paper is to review the available weigh-in-motion (WIM) data and update the live-load model accordingly. The considered WIM database includes over 200 million records, from 44 WIM locations around the USA, with about 1–2 million vehicles per year for each location. The actual number of records depends on traffic volume expressed in terms of an average annual daily truck traffic (AADTT) and local conditions, such as type of road, local facilities, seasonal factors. The cumulative distribution functions of load effect are extrapolated to predict the maximum expected live load for extended time periods. The statistical parameters of live load are determined for the static component of the live-load effect for one lane in simple-span bridges based on the actual WIM data. The analysis showed that there is a 15%–20% increase in live load in comparison to Ontario data. An updated design tandem is proposed to adequately represent the traffic load for short simply supported spans.

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What this paper is about

Twenty-five years ago, a bridge live-load model was developed along with the calibration of the AASHTO LRFD Specifications. The statistical parameters of truck loads were based on the Ontario, Canada, truck survey performed in the 1970s. The objective of this paper is to review the available weigh-in-motion (WIM) data and update the live-load model accordingly. The considered WIM database includes over 200 million records, from 44 WIM locations around the USA, with about 1–2 million vehicles per year for each location. The actual number of records depends on traffic volume expressed in terms of an average annual daily truck traffic (AADTT) and local conditions, such as type of road, local facilities, seasonal factors. The cumulative distribution functions of load effect are extrapolated to predict the maximum expected live load for extended time periods. The statistical parameters of live load are determined for the static component of the live-load effect for one lane in simple-span bridges based on the actual WIM data. The analysis showed that there is a 15%–20% increase in live load in comparison to Ontario data. An updated design tandem is proposed to adequately represent the traffic load for short simply supported spans.

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Available abstract

Twenty-five years ago, a bridge live-load model was developed along with the calibration of the AASHTO LRFD Specifications. The statistical parameters of truck loads were based on the Ontario, Canada, truck survey performed in the 1970s. The objective of this paper is to review the available weigh-in-motion (WIM) data and update the live-load model accordingly. The considered WIM database includes over 200 million records, from 44 WIM locations around the USA, with about 1–2 million vehicles per year for each location. The actual number of records depends on traffic volume expressed in terms of an average annual daily truck traffic (AADTT) and local conditions, such as type of road, local facilities, seasonal factors. The cumulative distribution functions of load effect are extrapolated to predict the maximum expected live load for extended time periods. The statistical parameters of live load are determined for the static component of the live-load effect for one lane in simple-span bridges based on the actual WIM data. The analysis showed that there is a 15%–20% increase in live load in comparison to Ontario data. An updated design tandem is proposed to adequately represent the traffic load for short simply supported spans.

Key concepts: Weigh in motion, Truck, Structural load, Span (engineering), Bridge (graph theory), Influence line, Traffic volume, Engineering

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