This study investigates the effect of car speed on the vibration transmitted to the passenger through both the seat pan and backrest. Ten male subjects sat in the front passenger seat of a sedan car and were driven at three different speeds (60, 80 and 100 km/h). During each ride, the vibration dose value (VDV) was measured in the x, y and z-directions on the seat pan and at the backrest. The results showed an increase in the VDV in all directions with increasing the car speed. The VDVs at the backrest were found as high as, or even higher than, those measured on the seat depending on the speed and the measurement axis. Hence, it is recommended that vibration in road vehicles be assessed using more than one speed while taking into consideration measurement at the backrest.
In this work, Artificial Neural Network (ANN) modelling has been employed to investigate the effects of various factors on the biodynamic responses to vibration represented by the transmissibility and its phase. These factors include, height, weight, Body Mass Index (BMI), age, frequency and posture. Nine subjects stood on a vibrating plate and were exposed to vertical vibration at nine frequencies in the range 17-46 Hz while adopting four different standing postures; Bent Knee posture (BK), Locked Knee posture (LK), right foot to the Front and left foot to the Back posture (FB) and One Leg posture (OL). The accelerations of the vibrating plate and the head of the subjects were measured during the exposure to vibration in order to calculate the transmissibility between the vibrating plate and the head. Genetic Algorithm (GA) was used to choose ANN’s number of hidden layers and number of neurons in each layer to obtain the best performance for predicting the transmissibility. The GA compared the root mean square errors (RMSE) between the ANN outputs and the experimental outputs, and then choose the best results that could be achieved. The number of hidden layers and number of neurons tested in GA vary from one hidden layer to four hidden layers, and from one neuron per layer to one hundred neurons per layer. Several runs have been conducted to train and validate the ANN model. The results show that double hidden layer with 13 neurons in the first layer and 12 neurons in the second layer give the best candidate. The proposed model can be integrated with whole-body vibration machines in order to choose the suitable exposure based on the user’s characteristics.
Productivity and concerns regarding the well-being of workers exposed to vibrations stand as significant topics within labor-intensive sectors. In particular, this study contributes to the existing research by analyzing the problem with linkages among worker skill level, production rates, and vibration exposure. A bi-objective mixed integer linear programming model was employed to optimize both productivity and the exposure to hand-arm vibration in the manufacturing workplace. A sensitivity analysis was carried out to examine the impact of key parameters on the trade-off between productivity and vibration exposure. The results demonstrate the model's effectiveness in determining the best job rotation schedules by achieving optimal productivity and vibration exposure for low and medium problem sizes. Moreover, the numerical case study points out that strengthening the workforce by adding more proficient skilled workers can maintain a good level of productivity with a decreased likelihood of excessive vibration exposure.
Abstract Absorbed power (AP) is a biodynamic response that is directly related to the magnitude and duration of vibration. No work has previously investigated the power absorbed by the standing human body during the exposure to vibration training conditions or otherwise. This article reports the power absorbed by the standing human body under whole-body vibration (WBV) training conditions. In this work, the force and acceleration used to calculate the apparent mass by Nawayseh and Hamdan (2019, “Apparent Mass of the Standing Human Body When Using a Whole-Body Vibration Training Machine: Effect of Knee Angle and Input Frequency,” J. Biomech., 82, pp. 291–298) were reanalyzed to obtain the AP. The reported acceleration was integrated to obtain the velocity needed to calculate the AP. The effects of bending the knees (knee angles of 180 deg, 165 deg, 150 deg, and 135 deg) and vibration frequency (17–42 Hz) on the power absorbed by 12 standing subjects were investigated. Due to the different vibration magnitudes at different frequencies, the AP was normalized by dividing it by the power spectral density (PSD) of the input acceleration to obtain the normalized AP (NAP). The results showed a dependency of the data on the input frequency as well as the knee angle. A peak in the data was observed between 20 and 24 Hz. Below and above the peak, the AP and NAP tend to increase with more bending of the knees indicating an increase in the damping of the system. This may indicate the need for an optimal knee angle during WBV training to prevent possible injuries especially with prolonged exposure to vibration at high vibration intensities.
This study investigates the effect of car speed on the vibration transmitted to the passenger through both the seat pan and backrest. Ten male subjects sat in the front passenger seat of a sedan car and were driven at three different speeds (60, 80 and 100 km/h). During each ride, the vibration dose value (VDV) was measured in the x, y and z-directions on the seat pan and at the backrest. The results showed an increase in the VDV in all directions with increasing the car speed. The VDVs at the backrest were found as high as, or even higher than, those measured on the seat depending on the speed and the measurement axis. Hence, it is recommended that vibration in road vehicles be assessed using more than one speed while taking into consideration measurement at the backrest.