Traffic Load Effects Research Articles

Discussion of “Effects of Traffic Loading on Seasonal Temperature Change-Induced Problems for Integral Bridge Approaches and Mitigation with Geosynthetic Reinforcement”

Discussion of “Effects of Traffic Loading on Seasonal Temperature Change-Induced Problems for Integral Bridge Approaches and Mitigation with Geosynthetic Reinforcement”

A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data

The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (R2) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in R2. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in R2 compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and R2 by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems.

Study of the Micro-Vibration Response and Related Vibration Isolation of Complex Traffic Load-Induced Experimental Buildings

In view of the high-sensitivity vibration effect of precision instrument laboratory buildings under the influence of surrounding traffic loads, field micro-vibration tests under various working conditions were carried out based on actual projects. Combined with numerical simulation, measured data served as input loads to simulate the vibration effect of various traffic loads on the floor of a building structure, and the structural vibration laws under the comprehensive action of various loads were analyzed. The vibration isolation effect of the isolation ditch on the oblique orthogonal load was investigated according to the corresponding safety index. The results show that the main frequency components of the site vibration frequency caused by various traffic loads are approximately 25 Hz and that the root-mean-square speed value is stable below VC-E, which meets the design requirements. Under the comprehensive action of multiple loads, the site structure will produce a vibration amplification effect, which is obvious when all types of loads are distributed symmetrically and the curve distribution is controlled by load factors with large amplitudes. The isolation effect of a small isolation ditch is best when it is located close to the source and the building. The depth of the isolation ditch must be greater than the maximum depth of the source to achieve better results, and the width has little influence. The effect of a small isolation trench on vertical vibration is poor, and the oblique orthogonal triaxial load has a counteracting effect on the vertical component. Thus, additional structural vibration isolation measures are needed. The research results provide a reference for engineering vibration isolation, damping measures, and structural design.

Fatigue damage assessment of a large rail-cum-road steel truss-arch bridge using structural health monitoring dynamic data

Structural health monitoring (SHM) system provides valuable information for the fatigue assessment of existing rail-cum-road bridges. This paper aims to develop an effective fatigue assessment approach for the rail-cum-road bridge, Jiujiang Yangtze River Bridge, by utilising the SHM data, focusing on dynamic modal analysis, finite element model updating and fatigue assessment. First, the traffic load spectrum and modal characteristics of the bridge are investigated from the SHM data. A three-dimensional finite element model is constructed and then updated by using the measured modal data through the proposed regularised model updating method. Then, the updated numerical model is verified with the measured dynamic response data, which can be utilised for calculating stress response at critical structural components of the rail-cum-road bridge. Finally, an improved Corten-Dolan’s model is proposed to analyse the fatigue damage and structural reliability of the critical structural components of the bridge, taking into account the combined effects of train and highway vehicle loads. The results demonstrate that the proposed fatigue assessment method provides more reliable results for the rail-cum-road bridge by considering the combined effect of multi-level traffic loads and the non-linear fatigue damage accumulation. It is concluded that the short H-shaped suspender is identified as the most vulnerable structural member of the rail-cum-road bridge, and the remaining fatigue service life of the typical components of the bridge should meet the design requirement.

Frost heave characteristics of subgrade silty clay affected by cyclic stress: Experiments and prediction model

To investigate the effects of traffic loads on frost heave behaviors, frost heave tests of silty clay soil were conducted using an improved temperature-controlled cyclic compression-shear device. This research employed three stress modes: vertical cyclic stress, horizontal cyclic shear stress, and complex cyclic stress that combines vertical cyclic stress with horizontal cyclic shear stress. Additionally, it considered the effects of the amplitude and frequency of complex cyclic stress. Test results show vertical cyclic stress densifies specimens and restrains vertical displacement development. Vertical cyclic stress's pumping effect promotes water absorption during frost heave. Horizontal cyclic shear stress can increase in-situ frost heave and induce minor consolidation than vertical cyclic stress, dramatically enhancing vertical displacement. Under complex cyclic stress conditions, vertical cyclic stress and horizontal cyclic shear stress at low amplitudes and frequencies enhance vertical displacement. The primary component that promotes the frost heave ratio is horizontal cyclic shear stress, which could lead to a looser frozen soil structure. Finally, an improved frost heave ratio prediction model was developed, considering the influences of vertical cyclic stress, horizontal cyclic shear stress, and loading frequency.

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Study of the effect of traffic and static loads on the stability and deformation of shallow urban utility tunnels

Study of the effect of traffic and static loads on the stability and deformation of shallow urban utility tunnels

Hierarchical Bayesian modeling of highway bridge network extreme traffic loading

The road network consists of bridges of various lengths and configurations, all of which require accurate prediction of traffic load within their lifetime. However, current prediction methods are limited to modeling and predicting traffic load for a handful of individual bridges only; no method can simultaneously model and predict the traffic load of all bridges within an entire road network. Further, conventional models neglect the information that exists in the traffic load effect data established for different bridges, leading to large estimation uncertainties for each bridge and load effect examined. This study proposes a hierarchical Bayesian model that can estimate the traffic load effect of multiple bridges simultaneously, and subsequently create predictions for the remaining (unexamined) bridges within the road network. The proposed model is demonstrated using the traffic load data and influence lines used in the background study for the Eurocode 1 Load Model 1. The results show significant reductions in prediction uncertainties, better fits as measured by leave-one-out statistics, more robust fits against extremes, and the emergence of intuitive correlation structures between different bridges’ traffic loads that are absent in conventional models. This paper also presents a potential new strategy to reduce estimation uncertainty, and a method to predict parameters and return levels for bridges across an entire network made possible by the proposed hierarchical Bayesian model.

Polyurethane-Asphaltic Concrete as an Alternative Solution for Road Pavement Distress – A Review

Road traffic accidents are the primary source of injuries and fatalities worldwide, accounting for 20–50 million long term disabilities and 1.25 million deaths annually. Pavement conditions rank as the second most common cause of traffic accidents. The combined effects of environmental factors and traffic loads lead to pavement discomfort. Ensuring proper pavement design and maintenance can assist in lowering accident rates and preventing long-term disability associated with drivers. Physical maintenance procedures like overlays, seal coatings, patching, and filling joints are carried out in practice; nevertheless, they only offer temporary fixes by treating the symptoms rather than the underlying issue. An alternative method for road pavement maintenance is using polyurethane to improve the properties of asphalt due to the excellent properties of polyurethane. This study conducts a systematic review of the polyurethane asphaltic characteristics and performance based on the previous study by researchers. It is found that the pavement maintenance work using polyurethane is able to sustain longer periods and is moisture resistant due to its excellent bonding with the existing pavement material. Keywords: polyurethane, asphalt, pavement, distress, road

Investigation on Anti-Fuel Erosion Performance of Sasobit/SBS-Modified Asphalt and Its Mixtures.

The fuel leakage of fuel vehicles will exacerbate the occurrence of distresses on asphalt pavements, including peeling, chipping and potholes, especially under the synergistic effect of traffic load and environment. In this research, Sasobit, which is commonly used as a warm agent in asphalt, is selected as the anti-fuel erosion agent and incorporated into SBS-modified asphalt and its mixtures. Diesel and gasoline are selected as the fuel erosion media. Sasobit/SBS-modified asphalt binder and its mixtures are investigated for fuel erosion. The rheological properties of bitumen and the mechanical properties of asphalt mixtures are assessed. The experimental findings show that the dissolution velocity of SBS-modified asphalt with 3% Sasobit is 0.2%/min for diesel erosion, while it is 1.7%/min for gasoline erosion, lower than the control sample without Sasobit. Meanwhile, the rutting factor of Sasobit/SBS-modified asphalt decreases less than that of the control sample without Sasobit. Furthermore, the mass loss ratio after the Cantabro test of Sasobit/SBS-modified asphalt mixtures is 1.2% for diesel erosion, while it is 6.8% for gasoline erosion, lower than that of the control sample without Sasobit. The results of the mechanical properties for asphalt mixtures demonstrate that Sasobit can enhance the anti-fuel erosion performance. Moreover, the research results of the Sasobit modification mechanism show that Sasobit can form a microcrystalline structure in SBS-modified asphalt, which subsequently improves the anti-fuel of asphalt and its mixtures. This research provides a reference for anti-fuel erosion assessment methods and solutions to improve the anti-fuel erosion of asphalt pavement.

A Review on the Evaluating the Influence of Employment Nano-Matekaolin and Additive Materials on the Improvement of Hot Mixture Asphalt Properties

In previous years, the world witnessed great urban development which increased interest in this aspect. To keep pace with this development, several things and new materials have been created to raise the standard of road construction. One of these things is the use of nanomaterials. Currently, the use of nanomaterials has become common to improve the asphalt mixtures which are used in the construction of flexible pavement because of the rise in traffic volumes and vehicles loads. This study deals with review of past related studies that improving the properties of asphalt mixtures through the use of nanometakaoline in the hot asphalt mixture in different proportions. Different proportions of nanometakaoline were used for the Hot Mixture Asphalt properties revised in these studies which were shown that the adding of nano-metakaoline led to improve the properties of Hot Mixture Asphalt to resist the traffic load and environmental effects. They used Marshall Test, indirect test, wheel track tests and others tests to exam the properties of improved Hot Mixture Asphalt and compared with the standard mixture (control).

Theoretical substantiation of system for organising preventive treatment of asphalt concrete road pavements

Introduction. Over the past 20 years, the fleet of vehicles in the Republic of Belarus has increased significantly, and, consequently, the intensity of traffic on roads with a proportional increase in wear and tear of their surfaces. The need to protect asphalt concrete pavements from premature destruction dictates the search for new technological solutions. To effectively protect asphalt concrete pavements from the complex effects of water and traffic loads in the autumn-winter and spring-winter periods, the author has developed and implemented one of the options for protecting asphalt concrete pavements of a highway – treating them with a hydrophobic preventive Protekt- 01composition.Materials and methods. The research used theoretical and practical results from the implementation of the developed hydrophobic prophylactic composition.Results. The article presents an overview of the state of the problem of preventing destruction of asphalt concrete pavements abroad and in the Republic of Belarus, an analysis of the measures taken to implement the developed hydrophobic preventive Protekt-01composition at transport infrastructure facilities, and also, based on the data obtained from production testing a ‘System for organizing preventive treatment’ has been proposed, which will be aimed at preventing the beginning and future destruction of the asphalt concrete pavement of highways and will be based on monitoring the technical condition of the control object, planning, organizing, implementing and managing preventive and repair measures using navigation equipment and software that makes it possible to optimize the balance of costs and road surface defects.Originality. Thus, based on the theoretical substantiation and the results of practical implementation of the developed composition, the following are proposed:- method for ensuring the operation of a control system for liquid road-building materials which enables to eliminate overconsumption of the distributed substance;- ‘System for organizing preventive treatment’, which enables to increase the service life of asphalt concrete pavement by 1.2–1.5 times.

Understanding the pavement project failures in Pakistan: identifying causes and solutions

Pavement deterioration is a major issue in Pakistan's road and highway infrastructure. The current study attempts to clarify the most important factors of road degradation in Pakistan using a questionnaire prepared and distributed to engineers, contractors, architects, surveyors, and project managers involved in road development and upkeep. The study had 147 replies. This data was collected from employees from various road construction sectors in Pakistan. Convenience sampling, a sort of non-probability sampling, is used for data gathering due to time and resource constraints. The data were analysed through SPSS and Microsoft Excel. The responses were gathered using a 10-point Likert scale. The standard (inadequate density in surface or sub-base) has the maximum rank of 9.1, while the lowest factor (use of naturally smooth uncrushed aggregate) is 2.0. Furthermore, the causes of deterioration were reorganised into eight consistent groups of pertinent causes. The first group (effect of traffic load and volume) has a severity level of 6.5, while the last group (effect of bond between layers) ranks 3.22 in group comparison. The study is helpful in identifying the causes of road deterioration in Pakistan and avoiding or mitigating their effects during design, construction, and maintenance through operation.

Investigation of the interfacial bonding of new and old pavement materials for pothole applications: Bonding materials optimization, mechanical properties and structural performance

Under the combined effects of heavy traffic loading and extreme environmental conditions, the failure issues of repaired potholes caused by insufficient interfacial bonding strength between the new and old pavement materials occur frequently, which severely impairs the durability of the repaired structure. This study aims to improve the interfacial bonding strength between the new and old pavement materials and prolong the service life of repair pothole materials. Firstly, the mechanical analysis was carried out using finite element simulation, and a technical measure was proposed to ensure improved interfacial bonding strength between new and old pavement materials. Secondly, emulsified asphalt (EA), light asphalt (LA), waterborne epoxy resin-modified emulsified asphalt (WER-EA) and silicone penetrant (SP) were selected to investigate the paving workability, viscosity changing and curing times of bonding materials at different temperatures. On this basis, the interlaminar shear and pull-out tests were performed under different paving mass, temperatures and humidity conditions, and the shear and pull-out strengths of four bonding materials were compared to optimize the type and paving mass of interface bonding material. The results show that an increase in bonding coefficient (BC) significantly reduces the values of pressure stress (σy) and pressure strain (εy), even the bucking depth of the pothole sides, thus decreasing the risk of pothole repair failure. Under normal or low-temperature conditions and field construction, SP has exceptional paving workability, and its shear and pull-out strengths are much higher than other bonding materials, with an optimum paving mass rate being 0.3 kg/m2 ∼ 0.4 kg/m2. The research results are of great significance in improving the pothole repairing level, standardizing the repair process and extending the service life of pavements.

Analysis of Pore Water Pressure Effect in Asphalt Patch Failure: An Integrated Modelling and Experiment Study

Moisture has been widely accepted as the main cause of asphalt patch failure, while the investigation of pore water pressure (PWP) in asphalt patch is limited. This research investigated the PWP generated in asphalt patch and quantified its impact on mechanical performance considering the inadequate compaction of asphalt patch. A hydro-mechanical model was first developed to analyze the PWP under field conditions, considering the effects of patch geometry, vehicle wandering, traffic loading, and temperature. The moisture-induced stress tester (MIST) was then used to condition patching materials under different PWP levels in a laboratory. After that, indirect tensile (IDT) and interface shear strengths of patching materials were evaluated to quantify the impact of PWP on material degradation. The numerical modeling results show that the maximum PWP can be within or at the interface of the asphalt patch, depending on the wheel path. The magnitude of PWP is related to the patch geometry, traffic load, and temperature. The maximum PWP, whether inside or at the interface of the asphalt patch, can be from 100 kPa to over 300 kPa under common field conditions. The laboratory experimental results suggest that the first 500 cycles of MIST conditioning led to over 30% decrease in both IDT and interface shear strengths of asphalt patch, and the strength ratio was much lower than the recommended level. These findings indicate that asphalt patches with poor compaction quality are very susceptible to moisture-induced damage, and the mechanical performance of patching material should be evaluated after conditioning with PWP effect.

Modeling damage caused by combined thermal and traffic loading using viscoelastic continuum damage theory

Various engineering models have been proposed to mitigate fatigue cracking associated with thermal and traffic loadings, which are the two primary fatigue cracking mechanisms that negatively affect the service life of asphalt pavement. Thermal fatigue cracking is induced by cycles of temperature changes with relatively long time periods, which can be regarded as slow frequency sinusoidal loading. Traffic fatigue cracking is induced by numerous vehicle load repetitions and is often characterized using fast frequency sinusoidal loading. These two loading types occur simultaneously in the field. In order to investigate how to properly model the damage due to these two loading types, a set of tests that vary the load, frequency, and mode of testing was designed to mimic the scenarios that asphalt pavements experience under thermal loading and traffic loading separately and under the combined and simultaneous effects of both thermal and traffic loading. The viscoelastic continuum damage (VECD) model was applied to simulate the changes in material integrity under each condition. After determining the appropriate configuration for the proposed prediction model, the material integrity predictions were found to be accurate for the fast frequency tests but less accurate for the slow frequency tests. This difference is attributable to plasticity being a much more significant factor under slow frequency conditions than under fast frequency conditions. For the tests combining both fast and slow frequency conditions, the material integrity results oscillated continuously. It was postulated that these oscillations were due to the recovery mechanisms of asphalt concrete such as nonlinearity and healing. When the simplified viscoelastic continuum damage (S-VECD) model was applied, which does not consider the material’s recovery characteristics, the predicted material integrity was able to match the overall trend observed in the tests. Ultimately, a new way to model combined damage simultaneously under two cracking mechanisms (thermal fatigue cracking and traffic fatigue cracking) is introduced in this paper.

A hybrid virtual–real traffic simulation approach to reproducing the spatiotemporal distribution of bridge loads

AbstractCurrent traffic simulation approaches analyze vehicle loads and load effects from a statistical perspective; however, they fail to reproduce the spatiotemporal distribution of bridge loads and resultant load effects at every moment, hindering real‐time bridge health management. This paper proposes a two‐step hybrid virtual–real traffic simulation (HvrTS) approach to reproduce the spatiotemporal distribution of bridge loads. Vehicle load sequences are first identified using computer vision based on traffic video from two surveillance cameras installed along the bridge. Next, traffic microsimulation is optimized with the identified vehicle load sequences from the two cameras serving as the known input and validated output. Using a cable‐stayed bridge under free‐flowing traffic, the HvrTS approach achieved a weighted mean square matching error of ≤0.3 m for the vehicle longitudinal location and a matching error of ≤7% for the vehicle lane position, whereas with congested traffic, the matching errors were much higher due to the inherent complexities and challenges associated with reproducing vehicle behavior in heavily congested situations. The traffic load effects calculated via HvrTS presented excellent spatiotemporal matching with those measured by a structural health monitoring system, especially in free‐flow traffic conditions. Applications on a continuous rigid frame girder bridge further validate these findings. Hence, the proposed HvrTS approach can overcome the challenge of spatiotemporal matching between vehicle loads and load effects in field monitoring.

EFFECTS OF CYCLIC TRAFFIC LOADS AND SEAWATER EROSION ON SUFFUSION OF CRUSHED CALCAREOUS SANDS

Calcareous sands, in contrast to ordinary terrestrial source sands, are characterized by their propensity for fragmentation. This leads to the fracturing of calcareous sands within the foundation under the impact of traffic loads. The crushed calcareous sands then experience suffusion due to cyclic wave action, potentially causing foundation settlement. However, limited research has been conducted on the effects of varying load frequencies and magnitudes on road foundations subjected to cyclic traffic loads. In this study, a series of numerical case studies employing the coupled computational fluid dynamics and the discrete element method (CFD-DEM) are conducted. The macroscopic and microscopic effects of load magnitude and frequency on fines loss mass, fines loss rate, soil surface displacement, and microstructure are analyzed. The results indicate that as the traffic load magnitude increases and frequency decreases, fines loss mass and volumetric strain of the soil decrease, reducing the suffusion effect on the foundation. These findings provide valuable insights for the development of micromechanical constitutive models for calcareous sands and the design of transportation infrastructure.

Prediction of the severity of exceeding design traffic loads on highway bridges

Being a driver of failure consequences, forecasting the severity of events where design traffic load limits on bridges have been exceeded (DLEEs) is fundamental for road safety. Previous research has focused on estimating failure consequences by direct and indirect cost metrics. Only recently has research assessed severity unconventionally, in which the type of DLEEs was predicted by applying econometric models through Binomial Logistic Regression (BLR). Since machine learning models using Artificial Neural Networks (ANN) have not yet been explored, this study will enhance the literature as follows. First, two different ‘severity’ models were set up as a function of bridge-side, temporal-context, and traffic load hazard variables. Whilst the former relied on a BLR, the latter used an ANN. Second, the performance of these models was assessed using confusion matrixes, some performance indicators, and a cross-entropy parameter. Raw Weigh-In-Motion data on 7.4 M+ individual vehicle transits on a bridge along a primary roadway in Brescia (Italy) were processed. Although a similarly strong performance was achieved for BLR and ANN, the results indicated that ANN was able to predict severity records with a higher level of confidence than BLR on the case study dataset, with the cross-entropy of the ANN less than one third of that of the BLR. These analyses can support road authority traffic management to safeguard bridges from traffic load hazards. Finally, this study recommends future developments, such as considering the structural effects of traffic loads in the modelling, prioritizing traffic management actions among bridges at network level, and exploring the impact of ANN models in risk assessment.

Sustainable Prioritization of Public Asphalt Paved Road Maintenance

The development of transportation infrastructure is crucial for a country's progress as it enables access to social and economic amenities and acts as a vital link between production and consumption. This is particularly significant for landlocked nations like Zambia, which heavily rely on a wide network of public highways, including both paved and unsurfaced roads. Regardless of their quality and construction, all road surfaces degrade over time due to the combined effects of traffic load and environmental factors. If not properly managed, this can lead to costly repairs and rehabilitation efforts. Government organizations face a dilemma when deciding which public roads to prioritize for maintenance. This follows that the inherent hurdle is competing needs within limited finances for infrastructure development. However, the challenge of road maintenance prioritization can be addressed by adopting a multi-criteria approach. This study aims to address the issue of sustainability and provides insights into which public roads should be given priority for maintenance. The research proposes a comprehensive set of criteria focusing on sustainability and assigns relative importance to them, with emphasis on state of deterioration, emergency function, climate resilience, environment, strategic importance, social considerations, economic factors, and political importance. It suggests that road maintenance decision-making should prioritize sustainability, resilience, and societal well-being, challenging traditional assumptions.