Truck Overloading Research Articles

Piezoresistive response of MWCNTs/Epoxy mixtures with load-sensing capability

The impact of automobiles on pavement infrastructure is escalating with the expansion of economic activity and road networks. Overloading of freight trucks has emerged as a significant challenge in the realm of heavy vehicle control and management, primarily due to the adverse effects on road surfaces and traffic safety. Self-sensing pavement materials offer the prospect of autonomous monitoring of forces exerted on the pavement without relying on embedded sensors. These materials are engineered by integrating conductive fillers into a polymer matrix to enhance their ability to detect stress or strain while preserving their mechanical properties. Nano-conductive materials possess the potential to revolutionize traditional paving materials in terms of performance and applications. In this study, epoxy resin was selected as the base material due to its favorable physical and chemical characteristics. Multi-walled carbon nanotubes (MWCNTs) were chosen as the conductive filler to formulate a MWCNTs/Epoxy mixture. The dispersion impact of the high-speed shear method on MWCNTs was verified. Subsequently, a hybrid resistance measuring method was developed using conductive copper foil as an electrode. An exploratory investigation was conducted to evaluate the piezoresistive response of MWCNTs/Epoxy mixtures under varying temperatures and loading frequencies. The study revealed that the MWCNTs content near the percolation threshold yields an improved piezoresistive response. The gauge factor of MWCNTs/Epoxy mixtures is up to 150, which is much higher than that of conductive asphalt mixture. Furthermore, the mixture exhibited robust and consistent piezoresistivity across different temperatures (ranging from 0°C to 60°C) and loading frequencies (ranging from 1 Hz to 10 Hz).

Weight Monitoring System for Truck Load

Abstract: For cargo to be moved safely and effectively, a truck load weight monitoring system is required Overloading of trucks not only poses a risk to road safety but also causes excessive wear and tear on the vehicle, resulting in higher maintenance costs andreduced fuel efficiency. To address these challenges, an advanced weight monitoring system has been developed to measure and monitor the weight of truck loads using HX711 loadcell. When it comes to monitoring and managing the weight of truck loads. Overloading of trucks has been a longstanding issue in the industry. It not only compromises road safety but also has detrimental effects on the vehicles themselves. Excessive weight can lead to increased braking distances, compromised steering control, and increased risk of tire blowouts, all of which contribute to higher accident rates. This system utilizes advanced technologies and sensors to accurately measure and monitor the weight distribution of cargo carried by trucks. By providing real-time weight information using GPS and GSM modules, operators and fleet managers can make informed decisions to ensure compliance with weight regulations and prevent overloading

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net.

Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety.

Stochastic Propagation of Fatigue Cracks in Welded Joints of Steel Bridge Decks under Simulated Traffic Loading.

The fatigue cracking of orthotropic steel bridge decks (OSDs) is a difficult problem that hinders the development of steel structures. The most important reasons for the occurrence of fatigue cracking are steadily growing traffic loads and unavoidable truck overloading. Stochastic traffic loading leads to the random propagation behavior of fatigue cracks, which increases the difficulty of the fatigue life evaluations of OSDs. This study developed a computational framework for the fatigue crack propagation of OSDs under stochastic traffic loads based on traffic data and finite element methods. Stochastic traffic load models were established based on site-specific, weigh-in-motion measurements to simulate fatigue stress spectra of welded joints. The influence of the transverse loading positions of the wheel tracks on the stress intensity factor of the crack tip was investigated. The random propagation paths of the crack under stochastic traffic loads were evaluated. Both ascending and descending load spectra were considered in the traffic loading pattern. The numerical results indicated that the maximum value of KI was 568.18 (MPa·mm1/2) under the most critical transversal condition of the wheel load. However, the maximum value decreased by 66.4% under the condition of transversal moving by 450 mm. In addition, the propagation angle of the crack tip increased from 0.24° to 0.34°-an increase ratio of 42%. Under the three stochastic load spectra and the simulated wheel loading distributions, the crack propagation range was almost limited to within 10 mm. The migration effect was the most obvious under the descending load spectrum. The research results of this study can provide theoretical and technical support for the fatigue and fatigue reliability evaluation of existing steel bridge decks.

Probabilistic vehicle weight estimation using physics‐constrained generative adversarial network

AbstractTraffic information plays an important role in the design and management of civil transportation infrastructure. Bridge weigh‐in‐motion (BWIM) provides an effective tool for traffic information gathering by estimating vehicle parameters including its weight through bridge responses. Most existing BWIM algorithms rarely consider the epistemic uncertainty of vehicle weight in terms of the probabilistic distribution of estimated axle weights (AWs) of the vehicle. This paper proposes a novel methodology for probabilistic vehicle weight estimation using a physics‐constrained generative adversarial network (GAN). Generative models are introduced to describe the probabilistic distributions of estimated AWs and bridge responses. Physics constraints on the generative models are formulated and enforced by minimizing a physics‐based loss function. The generative models are then learned by training a physics‐constrained GAN using the observed bridge responses. Numerical study and field testing are conducted to demonstrate the proposed method using representative highway bridges and vehicles. The results show that the proposed method can successfully capture the uncertainty in the vehicle weight estimation and provide the probabilistic distributions of the estimated AWs for different vehicle types and loading conditions considered, which can enhance the application of BWIM for relevant tasks such as traffic data collection and truck overloading enforcement. Based on the results obtained from the numerical study and field testing, the maximum coefficient of variation obtained for the AWs and gross vehicle weight of the presented cases are 0.55 and 0.11, respectively.

Comparison of the lifetime predicted by elastic analyses between two pavement structure candidates considering truck overloading

ABSTRACT In Thailand, crushed rock, which is a common material for the base layer of a flexible pavement structure, is becoming insufficient or has to be transported from distance. Lateritic soil is largely available, but its bearing capacity is not sufficient, unless improved with cement. For rural roads, thin-asphalt surface pavement structure with the crushed rock base is common. With increasing traffic volume and truck overloading, the pavement structure must be strengthened. In this study, the lifetime of this pavement structure type strengthened by increasing asphalt's thickness (Structure I) was compared with the one by replacing the crushed rock with the cemented lateritic soil for the base layer (Structure II). Both linear elastic analysis (LEA) and non-linear elastic analysis (NLEA) were performed for prediction of the lifetime, and then comparison was made. Stress state-independent elastic Young's moduli (E) of pavement structure materials were estimated from CBR test results and used in LEA, while stress state-dependent E were determined by triaxial compression tests and used in NLEA. It was found that LEA gives a shorter lifetime for Structure I, while NLEA for Structure II. Adding more cement to the base layer of Structure II results in a comparable lifetime to that of Structure I and the construction cost is significantly cheaper. However, with increasing of truckload, the lifetime of Structure II is shortened more rapidly than that of Structure I, and therefore more cement should be added to maintain a comparable lifetime.

Reliability Analysis of Prestressed Concrete Bridges in Mexico: Assessment and Live Load Factors Proposal

The methodologies to predict the safety levels of highway bridges must consider suitable live load models from real and critical traffic conditions. This research is focused on the reliability assessment of prestressed simply supported Mexican bridges, which are the most common structures for short and medium spans in Mexico. The safety levels of highway bridges are determined to show the impact generated by two different kinds of live models on using prestressed type AASHTO I beams in Mexico, with spans of 10, 15, 20, 25, 30 and 35 m. These safety levels are obtained using a probabilistic approach based on the reliability theory. Selected highway bridges are designed according those procedures proposed by the Mexican Institute of Transportation rules and AASHTO LRFD bridge specifications, which are commonly used by Mexican bridge designers. For all the cases, results of IMT-66.5 live load model achieve higher safety levels than T3-S2-R4 truck. Shear reliability analyses indicate high level overdesign using Mexico City design code, leading to high safety levels for all evaluated cases. For bending moment analysis, the bridges with spans higher than 20 m have reliability indices under 3.5 for both live load models and a traffic scenario with 35% trucks. Therefore, we propose new live load factors for bridges with spans longer than 20 m to achieve the target reliability index of 3.5. For traffic scenarios with 18% and 35% trucks, proposed live load factors for IMT 66.5 and T3-S2-R4 trucks have increments of 6.7% and 7.8%, and 16.7% and 16.8%, respectively. An overall methodology was also proposed to be applied in other places with similar truck overloading problems for evaluating new and current bridge infrastructure.

Dynamic Reliability of Continuous Rigid-Frame Bridges under Stochastic Moving Vehicle Loads

The current volume of freight traffic has increased significantly during the past decades, impacted by the fast development of the national transportation market. As a result, the phenomena of truck overloading and traffic congestion emerge, which have resulted in numerous bridge collapse events or damage due to truck overloading. Thus, it is an urgent task to evaluate bridge safety under actual traffic loads. This study evaluated probabilistic dynamic load effects on rigid-frame bridges under highway traffic monitoring loads. The site-specific traffic monitoring data of a highway in China were utilized to establish stochastic traffic models. The dynamic effect was considered in a vehicle-bridge coupled vibration model, and the probability estimation was conducted based on the first-passage criterion of the girder deflection. The prototype bridge is a continuous rigid-frame bridge with a midspan length of 200 m and a pier height of 182 m. It is demonstrated that the dynamic traffic load effect follows Gaussian distribution, which can be treated as a stationary random process. The mean value and standard deviation of the deflections are 0.071 m and 0.088 m, respectively. The dynamic reliability index for the first passage of girder deflection is 6.45 for the current traffic condition. However, the reliability index decreases to 5.60 in the bridge lifetime, accounting for an average traffic volume growth ratio of 3.6%.

Development of a Risk Assessment Module for Bridge Management Systems in New Jersey

Bridges are critical for the mobility of our society and its economic growth. Available funds for bridge repair, maintenance, and rehabilitation are limited. The Moving Ahead for Progress in the 21st Century Act (MAP-21) introduced several new parameters for improving the management of bridge assets, such as bridge element evaluation, life-cycle analysis, and risk-based performance indicators. Risk-based methods account for the uncertainties embedded into engineering variables and long-term evaluations. The objective of this paper is to identify, assess, and quantify structural risk components to bridges using probabilistic risk methodologies and data from the National Bridge Inventory database. The aim is to simplify the implementation of risk-based ranking procedures into bridge management system packages according to the MAP-21 vision. Therefore, machine learning techniques are employed to facilitate the introduction of probabilistic risk methods into bridge management systems. The procedure is described for seven hazards that are pertinent to bridges in New Jersey: overloading, fatigue, seismic, flooding, scour, vehicle and vessel collision. Risk values are computed in monetary terms to homogenize the comparison among bridges for different hazards. The analysis is performed on 5,534 bridges, showing that seismic events and fatigue resulting from truck overloading are the most dominant hazards in New Jersey, for which about 97.0% and 29.0% of bridges show some level of risk. The main limitation of the proposed framework is the lack of accurate data from bridge inventories necessary to thoroughly perform a fully structural probabilistic analysis of bridges and to minimize engineering judgment.

Overlay thickness estimation using falling weight deflectometer and benkelan beam method-A case study

Pavements with bituminous surfacing are the most preferred and commonly used type of surfacings in India, because of their lower initial cost, ease of maintenance and subsequent up-gradation through stage construction, if necessary. The high traffic intensity in terms of commercial vehicles, overloading of trucks and significant variations in daily and seasonal temperature of the pavement have been responsible for early development of various kinds of distresses leading to premature failure of bituminous surfacings. Due to the various distresses developed in flexible pavements, strengthening by overlay is sometimes required for improving the structural adequacy of the pavement. The determination of overlay thickness is generally carried out by using Benkelman Beam deflection method as per the procedure given in IRC:81-1997. In this method, a static load is applied to the pavement surface and rebound deflections as measured. However, the limitation of the Benkelman Beam technique is that the measurement of deflection under static load does not simulate loading conditions produced in pavements by a moving vehicle. Other equipment which can also be used for structural evaluation of pavements is Falling Weight Deflectometer (FWD). FWD is an impulse-loading device in which a transient load is applied to the pavement and the deflected shape of the pavement is measured. This data, combined with the measured impact load, may be back-analysed (using layered elastic theory) to determine the stiffnesses of the various layers, and the subgrade. In the present study, the requirement of overlay was determined through Benkelman Beam and FWD methods for a 4.5 km road length in Noida, UP in NCR region. A comparison was made between the overlay thicknesses obtained as per IRC:81-1997 and IRC:115- 2014 and the results are presented herein.

Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors.

The purpose of this paper is to investigate the existence of stratification heterogeneity in traffic accidents in Shenzhen, what factors influence the casualties, and the interaction of those factors. Geographical detection methods are used for the analysis of traffic accidents in Shenzhen. Results show that spatial stratification heterogeneity does exist, and the influencing factors of fatalities and injuries are different. The traffic accident causes and types of primary responsible party have a strong impact on fatalities and injuries, followed by zones and time interval. However, road factors, lighting, topography, etc., only have a certain impact on fatalities. Drunk driving, speeding over 50%, and overloading are more likely to cause more casualties than other illegal behaviors. Speeding over 50% and speeding below 50% have significant different influences on fatalities, while the influences on injuries are not obvious, and so do drunk driving (Blood Alcohol Concentration ≥ 0.08) and driving under the influence of alcohol (0.08 > Blood Alcohol Concentration ≥ 0.02). Both pedestrians and cyclists violating the traffic law are vulnerable to fatality. Heavy truck overloading is more likely to cause major traffic accidents than minibuses. More importantly, there are nonlinear enhanced interactions between the influencing factors, the combination of previous non-significant factors and other factors can have a significant impact on the traffic accident casualties. The findings could be helpful for making differentiated prevention and control measures for traffic accidents in Shenzhen and the method selection of subsequent research.

Failure Trend of Transport Infrastructure in Developing Nations: Cases of Bridge Collapse in Nigeria

Sustainable transport system must be supported by resilient infrastructure such as bridges. Bridge is an essential transportation structure that offers unique solutions for road and rail traffic to cross rivers, gorges and difficult ground conditions or for reducing conflict points in transportation system by carrying one mode of traffic over the other. Due to its pronounced economic importance to the society, bridge design aspects must go beyond the physical bridge structures and must cover all the factors that impact on the safe operation throughout its serviceable life span. The environment and the obstacle it crosses, the soil which carries it, the self-weight, imposed dead weights and the moving live loads must be adequately taken care of. The considerable conflicting factors of the site’s soil characteristics, river hydrology, ecosystem environmental impact, aesthetics, historical and archaeological impacts, permanent and transient loads, construction technologies and construction materials bring together a vast expertise of professionals. These sizeable factors must be well managed and harnessed by the vast team of experts involved to guarantee a positive outcome. The vast decay of Nigerian transport system is visible in all complimenting infrastructure including bridges. Cases of bridge collapse abound in Nigeria within the last decade. This research studies the failure trends of bridges in Nigeria and forty-five documented cases are considered for the research. Structural health monitoring approaches were combined with statistical measures to assess the causes and to proffer solutions to the failure trend. Poor maintenance, torrential rainfall/flooding, terrorist attacks, faulty design, poor materials and construction quality and truck overloading were found to be the major causes of bridge failures in Nigeria.

Use of regression trees to predict overweight trucks from historical weigh-in-motion data

Abstract The traffic of overloaded trucks is a critical problem in highways. It affects pavement performance life, reduces the service life of bridges, and has a negative impact on road safety, average speed and level of service. There are several practices to prevent the truck overloading issue, i.e., enforcement activities to verify the truck's compliance with the legal weight limits. This paper investigates the development of a method that uses available weigh-in-motion (WIM) data to identify overloaded truck weight and travel patterns. The proposed approach is based on regression trees method, a simple and easily understandable analytic tool used to build prediction models from a large set of data. An overall analysis of the overloaded truck regression tree model shows that the most important variable to classify and predict overloading is the truck type. Regarding the axle overloading, the most significant variable is the time of the day (most of the overloaded trucks travel at late night or early morning). The regression tree results can be used to optimize the efficiency of administration activities by planning truck enforcement operations based on the more critical scenarios. Also, the results improve the knowledge about the load characteristics of trucks, which can lead to more effective pavement management systems and more assertive pavement structure designs.

Probabilistic machine learning approach to bridge fatigue failure analysis due to vehicular overloading

With the rapid development of freight transportation, truck overloading becomes very common and severe, posing a great threat to the safety of bridges, and it can even result in bridge failure. Traditional approaches investigating the overloading-induced fatigue damage on bridges, such as finite element analysis (FEA) and reliability analysis, have proven to be computationally expensive and model dependent. In this study, the prediction of fatigue failure probability of bridges due to traffic overloading was investigated by using the feedforward neural network and the Monte Carlo method. Our results show that based on a finite set of training data for the bridge under consideration, the proposed machine-learning-based approach can assist in providing an instantaneous assessment of the fatigue failure probability with high accuracy.

Effects of Revised Toll‐by‐Weight Policy on Truck Overloading Behavior and Bridge Infrastructure Damage Using Weigh‐in‐Motion Data: A Comparative Study in China

Since 2000, overloaded trucks have caused more than 50 bridges to collapse in China. In an effort to ensure the structural safety and extend the service life of the highway infrastructure, the Chinese government has proposed a series of policies in the past decade to mitigate truck overloading. This study aimed at investigating the effects of China’s recently revised toll‐by‐weight policy on truck overloading behavior and bridge infrastructure damage using weigh‐in‐motion data that spanned seven years (January 2011 to March 2018) and two successive toll‐by‐weight policies (with the new one implemented from August 2016), wherein truck data were measured from a typical national freeway segment. We first compared truck traffic volumes, compositions, and weight distributions under the initial and revised toll‐by‐weight policies. Next, we compared bridge infrastructure performance with respect to safety and fatigue based on the overloaded truck traffic observed under the initial and revised toll‐by‐weight policies. The results indicated that the revised toll‐by‐weight policy, which uses a stepwise incremental fee structure based on vehicle weight, was more effective at controlling truck overloading behavior and reducing bridge infrastructure damage than the initial toll‐by‐weight policy. Under the current policy, average daily truck volumes, overloaded truck proportions, and maximum truck weights decreased significantly. Concurrently, extreme and equivalent load effects for safety and fatigue assessments, respectively, decreased by an average of 20% for small‐ to medium‐span bridges. Despite these noted improvements, overloaded truck traffic persisted, with loads often exceeding bridge design levels. This study’s findings can support future efforts by the Chinese government to further refine their toll‐by‐weight policies and subsequently ensure a safe and viable transportation network.

Evolution of the Deflection According to Truck Overloading—Comparison between Benkelman Beam Method and FEM of Lateritic Pavements

The study of the deflection due to the passage of an axle on a pavement structure has the advantage to make possible to pronounce on the portance, the rigidity and the homogeneity of this one. In the case where the allowed axle load is not respected, surface deflection leads to premature deterioration of the roadway. In order to study the evolution of these deformations, deflection measurements were made by using the Benkelman method in the Fatick-Kaolack road in Senegal by varying the axle load with the following values: 10, 13 and 16 tons on three different zones. The results show a linear evolution of the deflection as a function of the axle load. Also, the impact of truck overloading was studied by considering different values of the axle load with comparison to the allowed axle load by using frequent types of vehicle. A numerical simulation of the Cast3M pavement was done first, in the case of an isolated single wheel with a radius of 12.5 cm, then in the case of an equivalent dual wheel with a radius of 18.1 cm for loads of 10 to 19 tons. In the same way, the pavement design software (ALIZE) is used to analyse the variation of the axle load. The results show a linear evolution of the deflection, which corroborates the measurements made in the field and those obtained by Samb (2014) with a slope coefficient equal to 1.7. Nevertheless, the case of an isolated single wheel of 12.5 cm radius is closest to the measurements with a straight line of slope coefficient equal to 1.9.

Cost-effectiveness of enforcing axle-load regulations: The Douala-N’Djamena corridor in Sub-Saharan Africa

Road conditions in Sub-Saharan Africa are typically poor, and only a subset of the newly constructed or rehabilitated roads reach their design life. Truck overloading generally causes this rapid deterioration. In Africa, there are few success stories on the imposition of axle-load limits. This study examines the existing regulations on the Douala-N’Djamena international road, which is the main transport corridor in Central Africa and the backbone for internal transport in Cameroon. It benefits from the detailed existing weighing data recorded since 1998 in the corridor’s 10 weighing stations. This vast amount of traffic data, together with available information on road structure and deterioration over time, has been used to conduct an accurate calculation of load equivalency factors. The HDM 4 model has been applied to three scenarios between 2000 and 2015: (1) no axle-load control, (2) the real situation and (3) no overloading tolerance. Results show that axle-load regulations have been reasonably well applied in Cameroon and have contributed to maintaining the corridor in fair condition. In spite of the fact that significant traffic increases are presently counterbalancing the damage avoided by axle-load limits, benefits provided by axle-load control have been substantial: in the period of 2000–2015, every € invested or spent on axle-load control has generated more than €20 of savings in road user costs and in road maintenance and rehabilitation expenditure, which represents, in absolute terms, more than €500 million.

Bridge rating modification to incorporate fatigue damage from truck overloads

Bridge rating modification to incorporate fatigue damage from truck overloads

Fatigue Design of Steel Bridges Considering the Effect of Dynamic Vehicle Loading and Overloaded Trucks

AbstractThe current practice for bridge-fatigue design may have underestimated the effect of dynamic vehicle loading and truck overloading on the fatigue life of steel bridges. In this study, a new approach for fatigue design of steel bridges was proposed that considers the effect of these two factors more rationally. A three-dimensional vehicle–bridge coupled model was developed to simulate the interaction between the bridge and vehicle. A simply supported steel I-girder bridge was used as an example for illustration of the proposed approach. The fatigue vehicle model was adopted from the AASHTO LRFD code, and overloading was considered by increasing the gross weight of the truck model. Numerical simulations were conducted to study the influence of three important parameters—road surface condition (RSC), vehicle speed, and truck gross weight—on the fatigue damage of the bridge considered. The results show that the RSC and truck gross weight both have a significant impact on the bridge-fatigue damage. By ...

Evaluation of the Impact of Truck Overloading on Flexible Compacted Gravel Lateritic Soil’s Pavements by FEM with Cast3M<sup>©</sup>

Within the framework of a FEM of the nonlinear behavior of lateritic pavement of Senegal, the effect of truck overloading is studied to estimate its impact on the deformability of road pavement on compacted gravel lateritic soils. For that purpose, various loading conditions were tested to measure the impact on the critical response parameters of road pavement design. The implementation of the models was realized with Cast3M©. This study allowed us to point out that the observed variations are linear and would help to plan in advance the impact of axle overloads for a better evaluation within the framework of the mechanistic (M. E.) design of pavements.