Road pavement collapse from overloaded trucks due to traffic diversion: A case study of Minna-Kateregi-Bida Road, Nigeria

Alhaji, Mustapha Mohammed; Alhassan, Musa; Adejumo, Taiye W.; AbdulKadir, H. K.

doi:10.1016/j.engfailanal.2021.105829

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…Trucks exceeding the legal axle and gross vehicle weight limit may cause serious damage to infrastructure [14,15] and increase the risk of traffic crashes [2,16]. For example, the high traffic volume of overloaded trucks led to the sudden failure of road pavement structures in Niger State, Nigeria, in 2020 [17]. Notably, 62% of trucks in truck collisions were overloaded in the United Arab Emirates [15].…”

Section: Introductionmentioning

confidence: 99%

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

Yang

2024

Sustainability

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Dangerous or illegal driving may disrupt the traffic safety management of public security organs, damage road infrastructure, lead to traffic accidents, or result in economic losses. This paper proposes a framework based on China’s highway toll data to identify dangerous or illegal driving risks, such as unfamiliarity with road conditions, overload, driving over the speed limit, fatigued driving, fake license plates, and other risks. The unfamiliarity with road conditions is identified with the frequency of driving routes. When the total weight of a vehicle and its cargo is greater than the upper limit of the total weight of the vehicle and its cargo, the vehicle can be judged as overloaded. When the actual travel time is less than the minimum travel time, it can be inferred that the vehicle has a risk of fatigued driving, driving over the speed limit, a fake license plate, or other risks. Two accidents are used to demonstrate the process of the proposed framework for identifying driving risks based on China’s highway toll data. Additional analysis proves that the proposed framework can be used to identify dangerous or illegal driving risks, and it provides a scientific basis for the traffic safety management of public security organs, reducing infrastructure damage, and avoiding the loss of national taxes and fees.

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Section: Introductionmentioning

confidence: 99%

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

Yang

2024

Sustainability

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“…It determines the rate of a country's socioeconomic development. It is important to note that without a good road, no economy can flourish [1,2]. However, roads contribute significantly to the growth of a nation's gross domestic product.…”

Section: Introductionmentioning

confidence: 99%

Influence of Land use and Traffic on Road Safety Along Jimeta Bypass

2024

Rafidain J. Eng. Sci.

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Many roads in some urban areas in developing countries, such as Nigeria, are characterized by a high level of traffic and, because of limited resources, receive inadequate forms of protection and inappropriate maintenance strategies. Some of the roads have become part of a landfill dump embankment, which cause defects that contribute to road accidents. The study sought to investigate the effect of land use and traffic on road safety along the Jimeta Bypass Road. Vehicle enumeration was carried out by manual counting. Road traffic accident data for the analysis was obtained from the Federal Road Safety Commission in Yola. Physical inspection of the pavement section revealed that it was in an unsuitable condition for riding and safety. The findings also revealed that the road pavement had an annual average daily traffic volume of about 1470 vehicles per day, with tricycles having the highest traffic volume and luxurious buses having the lowest traffic volume. Analysis of variance showed that there was no significant variation in traffic volume between periods (morning, afternoon, and evening); traffic only differs between vehicle types. Accident data collected was found to highly correlate with traffic volume, an indicator that the high traffic volume along the Jimeta Bypass Road could be responsible for the occurrence of road traffic crashes along the road. The study findings also indicate there is need to adopt the maintenance culture by Road Authorities and relevant stakeholders so as to address the failure condition at the onset.

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“…Additionally, with the continuous development of the road industry, the phenomenon of overloading heavy vehicles has increased [ 3 , 4 , 5 ]. This has led to a significant reduction in the service life of roads, increased the workload and cost of highway management and maintenance, and also indirectly increased the risk of traffic accidents [ 6 , 7 , 8 , 9 , 10 ]. In this background, road axle load weighing has become a very important part of road infrastructure development [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Weigh-in-Motion Pavement Sensor Based on Self-Sensing Nanocomposites for Vehicle Load Identification: Development, Performance Testing, and Validation

Liang

Zhang

Jiao

et al. 2023

Sensors

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The development of the transportation industry has led to an increasing number of overloaded vehicles, which reduces the service life of asphalt pavements. Currently, the traditional vehicle weighing method not only involves heavy equipment but also has a low weighing efficiency. To deal with the defects in the existing vehicle weighing system, this paper developed a road-embedded piezoresistive sensor based on self-sensing nanocomposites. The sensor developed in this paper adopts an integrated casting and encapsulation technology, in which an epoxy resin/MWCNT nanocomposite is used for the functional phase, and an epoxy resin/anhydride curing system is used for the high-temperature resistant encapsulation phase. The compressive stress-resistance response characteristics of the sensor were investigated by calibration experiments with an indoor universal testing machine. In addition, the sensors were embedded in the compacted asphalt concrete to validate the applicability to the harsh environment and back-calculate the dynamic vehicle loads on the rutting slab. The results show that the response relationship between the sensor resistance signal and the load is in accordance with the GaussAmp formula. The developed sensor not only survives effectively in asphalt concrete but also enables dynamic weighing of the vehicle loads. Consequently, this study provides a new pathway to develop high-performance weigh-in-motion pavement sensors.

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Road pavement collapse from overloaded trucks due to traffic diversion: A case study of Minna-Kateregi-Bida Road, Nigeria

Cited by 13 publications

References 16 publications

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

Influence of Land use and Traffic on Road Safety Along Jimeta Bypass

Novel Weigh-in-Motion Pavement Sensor Based on Self-Sensing Nanocomposites for Vehicle Load Identification: Development, Performance Testing, and Validation

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