Browsing by Author "Van der Spuy, Pierre Francois"

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    Derivation of a traffic load model for the structural design of highway bridges in South Africa
    (Stellenbosch : Stellenbosch University, 2020-03) Van der Spuy, Pierre Francois; Lenner, Roman; O'Brien, Eugene; Casas, Joan Ramon; Retief, Johan; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.
    ENGLISH ABSTRACT: This study sets out to derive a new traffic load model for the design of highway bridges in South Africa, with novel contributions to the field of bridge traffic loading. The current code for bridge design in South Africa, Technical Methods for Highways 7 (TMH7), was published in 1981 and was shown by previous studies, and by this study, to be deficient at characteristic level. This is especially true for shorter spans. TMH7 does not give any indication of the levels of safety used to calibrate the code and it is therefore not clear whether the code is still providing the necessary safety margins. Several studies, outlined in this document, show that the Uniformly Distributed Load (UDL) and knife edge loads for type NA loading should be increased. NA is referred to in TMH7 as normal loading. Further to this, the legal limit for Gross Vehicle Weight (GVW) was increased to 56 t and the vehicle characteristics on our roads have changed significantly since 1981. TMH7 loading is widely regarded in industry as too complex to apply and engineers have called for a simplified load model. A study of this nature is therefore well motivated to ensure safety of road users and to increase design efficiency for bridge engineers. Derivation of traffic load models requires measured traffic data. Previous studies showed that at least one year of Weigh in Motion (WIM) data is required to make accurate predictions of load effects at long return periods. Most WIM sensors in South Africa are located on National Route 3 (N3) and National Route 4 (N4) which are the major import and export routes in the country and which also carry the heaviest traffic. Stations along these routes are considered to be well calibrated. A WIM station along the N3 at Roosboom is chosen for this study, as seven years of traffic from 2010 to 2016 are available and the station is considered one of the heaviest loaded in the country. A comparison with other stations confirms this. In contrast with TMH7, it is typical in international codes to provide a load model for the slow, or heavy, lane which is reduced transversely by Multiple Lane Factors (MLFs). To align with international norms, a slow lane model is derived in this study based on the seven years of data at the Roosboom station as discussed previously. This measurement record includes the identification of 12.5 million heavy vehicles. The slow lane in the direction from Durban to Johannesburg is studied as vehicles in this direction are heavier than vehicles travelling from Johannesburg to Durban. Span lengths that are investigated range from 5 m to 50 m in increments of 5 m. The model derived herein is not valid for span lengths outside these bounds. The load effects (LEs) that are investigated are hogging on two span structures and sagging and shear on single span structures. For characteristic loads a 5 % probability of exceedance in a 50 year reference period is selected, similar to the Eurocode and the South African building design codes. This leads to a characteristic return period of 975 years. A censored GEV distribution is introduced to model the LEs. The shape factor is almost always negative, indicating an underlying Weibull distribution. This confirms the finding of other researchers that traffic LEs are bounded. The characteristic axle load amounts to 160 kN, which is used to calculate a UDL to replicate the characteristic load effects, resulting in a slow lane load model with a UDL of 13 kPa and a triple axle of 160 kN, spaced at 1.2 m. To distribute the slow lane model transversely, it is necessary to derive MLFs which take into account the reduced probability of simultaneous heavy vehicles in adjacent lanes. A novel method is presented in this work in which multiple lane WIM data is used to calculate MLF factors. A WIM station in Pretoria at Kilner Park measures four lanes of traffic at 0.01 s accuracy. This is the only station in South Africa measuring more than two lanes. By studying concurrent characteristic LEs in adjacent lanes it is possible to determine MLFs, first for two lanes loaded, then three lanes loaded and finally for four lanes loaded. The resulting MLFs are 1.0; 0.78; 0.07; 0.00. This implies that traffic from the fourth lane does not contribute to the characteristic global LEs. Vehicles that travel at speed, referred to as free flowing traffic, cause additional forces on bridge decks due to dynamic interaction between the vehicles and a bridge (Vehicle Bridge Interaction - VBI). To account for these increased loads, it is typical to multiply the static loads by a dynamic amplification factor (DAF) which is defined as the ratio between the total load effect to the static load effect. It is not the aim of this study to do an in depth investigation of dynamic amplification for South African bridges and it is therefore decided to adopt the values given in the ARCHES report D10, which are based on European traffic. It is reasonable to assume that South African roads conform to at least class B road profiles, implying a DAF of 1.4 up to 5 m span length and reducing linearly tot 1.2 at a 15 m span length. Seeing that South African vehicles are heavier than in Europe and have more axles, it is reasonable to assume that the DAF for South African traffic would be lower than for Europe. The ARCHES values can therefore be considered to be conservative in the absence of a comprehensive VBI study and measurements. To derive a design load model, it is necessary to establish Partial Factors (PFs) in accordance with structural reliability theory. Target 50 year β values are taken in accordance with the South African building design codes, which are based on extensive studies of historical practise in South Africa. For Ultimate Limit State (ULS), the 50 year β value is taken as 3.5 for a high consequence of failure and for Serviceability Limit State (SLS) as 1.5. The SLS value is in accordance with international standards. The reliability index is directly related to the probability of failure and hence it is possible to determine return periods of 435 years for SLS and 5040 years for ULS. For traffic loads, where the return periods for static loads are long, the probabilities of non-exceedance are close to 1.0 for characteristic, SLS and ULS. This leads to very small differences in load effects between characteristic and ULS return periods, especially when a censored GEV distribution is fitted which tends towards the Weibull distribution. When the LEs are near the bound of the fitted underlying Weibull distributions then there is hardly any uncertainty in the loading and all the uncertainty is located in the resistance. A new approach is introduced to address statistical uncertainty in fitting parameters. As seven years of data is used it is not surprising to find very small statistical uncertainty. Final partial factors are a function of reliability based partial factors, model uncertainty and statistical uncertainty. These amount to 1.18 for SLS and 1.33 for ULS. Chapter 8 presents a worked example for a typical bridge configuration for various widths and span lengths and considers both characteristic loads and ULS. The findings from this section are that the new model with DAF is always critical for all deck widths, for all span lengths and load effects when compared to normal loading in TMH7. The new model also exceeds LM1 in the Eurocode at characteristic and ULS levels. Although TMH7 abnormal and super loading is compared to the new model, it should be compared to a separate new model for abnormal loading which is outside the scope of this study.