Inefficient packaging constitutes a global problem that costs hundreds of billions of dollars, not to mention the additional environmental impacts. An insufficient level of packaging increases the occurrence of product damage, while an excessive level increases the packages' weight and volume, thereby increasing distribution cost. This problem is well known, and for many years, engineers have tried to optimize packaging to protect products from transport hazards for minimum cost. Road vehicle shocks and vibrations, which is one of the primary causes of damage, need to be accurately simulated to achieve optimized product protection.Over the past 50 years, road vehicle vibration physical simulation has progressed significantly from simple mechanical machines to sophisticated computer-driven shaking tables. There now exists a broad variety of different methods used for transport simulation. Each of them addresses different particularities of the road vehicle vibration. Because of the nature of the road and vehicles, different sources and processes are present in the vibration affecting freight. Those processes can be simplified as the vibration generated by the general road surface unevenness, road surface aberrations (cracks, bumps, potholes, etc.) and the vehicle drivetrain system (wheels, drivetrain, engine, etc.).A review of the transport vibration simulation methods is required to identify and critically evaluate the recent developments. This review begins with an overview of the standardized methods followed by the more advanced developments that focus on the different random processes of vehicle vibration by simulating non-Gaussian, non-stationary, transient and harmonic signals. As no ideal method exists yet, the review presented in this paper is a guide for further research and development on the topic.

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The characterization of transportation hazards is paramount for protective packaging validation. It is used to estimate and simulate the loads and stresses occurring during transport that are essential to optimize packaging and ensure that products will resist the transportation environment with the minimum amount of protective material. Characterizing road transportation vibrations is rather complex because of the nature of the dynamic motion produced by vehicles. For instance, different levels of vibration are induced to freight depending on the vehicle speed and the road surface; which often results in non-stationary random vibration. Road aberrations (such as cracks, potholes and speed bumps) also produce transient vibrations (shocks) that can damage products. Because shocks and random vibrations cannot be analysed with the same statistical tools, the shocks have to be separated from the underlying vibrations. Both of these dynamic loads have to be characterized separately because they have different damaging effects. This task is challenging because both types of vibration are recorded on a vehicle within the same vibration signal.This paper proposes to use machine learning to identify shocks present in acceleration signals measured on road vehicles. In this paper, a machine learning algorithm is trained to identify shocks buried within road vehicle vibration signals. These signals are artificially generated using non-stationary random vibration and shock impulses that reproduce typical vehicle dynamic behaviour. The results show that the machine learning algorithm is considerably more accurate and reliable in identifying shocks than the more common approaches based on the crest factor. TPR, true positive rate; FPR, false positive rate; AUC, area under the ROC curve. USING MACHINE LEARNING TO DETECT SHOCKS IN VIBRATION SIGNAL

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.

With ride quality being one of the most sought-after characteristics of a road bicycle by customers as well as by bicycle manufacturers, the vibrational behaviour of the bicycle/cyclist system has grown into an active field in sport engineering research in recent years. When assessing this behaviour, bicycle transmissibility and ride comfort, controlling test conditions to obtain repeatable load and acceleration measurements at the cyclist's contact points with the bicycle cannot be overemphasized. Surprisingly however, this consideration has not yet been specifically addressed in the literature. The aim of this paper is a first effort to investigate the effect of a selected set of test conditions on the measurement of vibration induced to the cyclist by a road bicycle. Our results showed that all the test conditions selected had a significant effect on the level of vibration induced to the cyclist.

Improving comfort in road bicycle design is a paramount concern for cyclists, who are affected by the vibrations caused by constant contact with the road surface. The cycling community has deployed many efforts in the attempt to understand and improve bicycle comfort. However, these attempts have been focused on specific components such as the fork, frame and wheels without knowing their relative influence on vibration induced to the bicyclist (VIB). The objective of this paper is to assess the relative contribution of bicycle components on the VIB at the cyclist's hands and buttocks. A factorial design test comparing the VIB in acceleration, force and power of different bicycle components has already shown that the handlebar and fork are the preponderant components for the VIB measured at the cyclist's hands. At the buttocks, the preponderant components are the wheels and frame.

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