ORCID Profile
0000-0003-3682-846X
Current Organisation
Université Laval
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: MDPI AG
Date: 28-09-2020
DOI: 10.3390/S20195564
Abstract: Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
Publisher: Wiley
Date: 28-04-2022
DOI: 10.1002/PTS.2655
Abstract: Slip‐preventing materials are often used as tertiary packaging to improve cargo securement and simplify truck loading. There are many national and international standards on their safe usage based on the extra friction they can provide. However, it is challenging to quantify the coefficient of friction (CoF) of slip‐preventing materials. This paper presents CoFs of three slip‐preventing materials and one baseline condition measured on different surfaces and contact pressures. These measurements show interesting phenomena that affect the result variability. For instance, 60% of the pairs of surfaces tested were affected by the contact pressure. Hence, the load of the freight seems to affect performance of the slip‐preventing materials. This interaction happened in different ways such as changes in the friction mode (constant slip and stick slip at difference frequencies and magnitudes). The typical CoF values available in standards as well as standardized measurement methods do not consider very well these effects nor how to deal with high variability of CoF measurements. This can lead to either over or under CoF estimations. To overcome these limitations, this paper presents a statistical method to quantify slip‐preventing material CoFs based on variability and confidence intervals of the measurements.
Publisher: SAGE Publications
Date: 18-01-2023
DOI: 10.1177/03611981221145125
Abstract: The effects of pavement characteristics on rolling resistance of heavy vehicles have gained more interest in recent years. Rolling resistance is the result of the combination of independent (but sometimes correlated) physical phenomena that dissipate energy, which can be regrouped under three different main themes. Road roughness (wavelengths between 0.5 and 50 m) causes movements in vehicle suspensions, which dissipate energy. Pavement macrotexture (wavelengths between 0.5 and 50 mm) creates additional viscoelastic deformations on tire treads. The viscoelastic behavior of the flexible pavement structure, which is referred to as structure-induced rolling resistance, is responsible for a perpetual upward slope perceived by heavy vehicle tires. Secondary aspects can also affect rolling resistance, such as road wetness and snow. This paper addresses each of these three main phenomena from three angles of analysis: (1) theoretical modeling, (2) laboratory experiments, and (3) in situ measurements. The literature on road roughness and structure-induced rolling resistance modeling is extensive compared to macrotexture-effect modeling, as the underlying physical mechanisms are still not well understood. There is, however, strong experimental evidence that the pavement macrotexture can significantly affect rolling resistance, but these studies are mostly related to cars. There are many in situ approaches, but the results are usually based on an indirect method and the different studies are difficult to compare and sometimes inconsistent. It appears that the bottleneck of scientific research on this topic is the fundamental inability to measure the rolling resistance of heavy vehicles with a direct in situ approach under real driving conditions.
Publisher: Elsevier BV
Date: 07-2023
Publisher: MDPI AG
Date: 11-10-2018
Abstract: The ability to characterize shocks which occur during road transport is a vital prerequisite for the design of optimized protective packaging, which can assist in reducing cost and waste related to products and good transport. Many methods have been developed to detect shocks buried in road vehicle vibration signals, but none has yet considered the nonstationary nature of vehicle vibration and how, in idually, they fail to accurately detect shocks. Using machine learning, several shock detection methods can be combined, and the reliability and accuracy of shock detection can also be improved. This paper presents how these methods can be integrated into four different machine learning algorithms (Decision Tree, k-Nearest Neighbors, Bagged Ensemble, and Support Vector Machine). The Pseudo-Energy Ratio/Fall-Out (PERFO) curve, a novel classification assessment tool, is also introduced to calibrate the algorithms and compare their detection performance. In the context of shock detection, the PERFO curve has an advantage over classical assessment tools, such as the Receiver Operating Characteristic (ROC) curve, as it gives more importance to high- litude shocks.
Publisher: SAGE Publications
Date: 27-02-2019
Abstract: Road surface imperfections and aberrations generate shocks causing vehicles to sustain structural fatigue and functional defects, driver and passenger discomfort, injuries, and damage to freight. The harmful effect of shocks can be mitigated at different levels, for ex le, by improving road surfaces, vehicle suspension and protective packaging of freight. The efficiency of these methods partly depends on the identification and characterisation of the shocks. An assessment of four machine learning algorithms (Classifiers) that can be used to identify shocks produced on different roads and test tracks is presented in this paper. The algorithms were trained using synthetic signals. These were created from a model made from acceleration measurements on a test vehicle. The trained Classifiers were assessed on different measurement signals made on the same vehicle. The results show that the Support Vector Machine detection algorithm used in conjunction with a Gaussian Kernel Transform can accurately detect shocks generated on the test track with an area under the curve (AUC) of 0.89 and a Pseudo Energy Ratio Fall-Out (PERFO) of 8%.
Publisher: Elsevier BV
Date: 2019
DOI: 10.1016/J.JBIOMECH.2018.10.025
Abstract: The dynamic properties of instrumented treadmills influence the force measurement of the embedded force platform. We investigated these properties using a frequency response function, which evaluates the ratio between the measured and applied forces in the frequency domain. For comparison, the procedure was also performed on the gold-standard ground-embedded force platform. A predictive model of the systematic error of both types of force platform was then developed and tested against different input signals that represent three types of running patterns. Results show that the treadmill structure distorts the measured force signal. We then modified this structure with a simple stiffening frame in an attempt to reduce measurement error. Consequently, the overall absolute error was reduced (-22%), and the error in force-derived metrics was also sufficiently reduced: -68% for average loading rate error and -80% for impact peak error. Our procedure shows how to measure, predict, and reduce systematic dynamic error associated with treadmill-installed force platforms. We suggest this procedure should be implemented to appraise data quality, and frequency response function values should be included in research reports.
Publisher: Wiley
Date: 26-02-2021
DOI: 10.1002/PTS.2563
Publisher: MDPI AG
Date: 25-03-2022
Abstract: Access to food remains a critical issue in Sub-Saharan Africa. In fact, 24.1% of its population suffers from undernourishment, and malnutrition affects more than a third of children under five years old. This problem will be exacerbated as the Sub-Saharan African population is predicted to double by 2050. To address this problem, it is imperative to meaningfully improve accessibility of fruits and vegetables for the population.They are an excellent source of vitamins and minerals that can fight malnutrition. Fruit and vegetable accessibility can be improved by reducing losses, which are estimated on average to be 50%. A literature review shows that there are many areas where solutions can be implemented to reduce these losses. These areas, in order of decreasing occurrence in the literature are: Cold storage, harvesting methods and pre-storage treatments, packaging, transport to markets and the sale stage. The reduction of food waste in SSA involves the establishment of better practices in all these areas. After analysis, it emerges that packaging should generate more interest due to its comparative ease of implementation to support other technologies like cold storage. Packaging made from agricultural waste or non-consumable materials should be highlighted to prevent pollution issues. This solution, in addition to offering a strong potential to fight against pollution, could also increase farmers’ income.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Julien Lepine.