Vibrational health risk assessment for truck operators in mining using artificial neural network

Operators of mining vehicles are frequently exposed to harmful levels of whole-body vibration (WBV). Long time exposure to WBV causes backache and has non-ergonomic effects on the human body. Exposure levels of the WBV have already been evaluated for different vehicles. Among these vehicles, mining trucks usually operate at the various working phases and also in different haul road conditions. This paper aims to develop a simultaneous integrated model to predict the WBV exposure for mining truck drivers. Considering the effect of the speed level, weight and geometry of load on the WBV exposure for the mining truck drivers are limited. There is not much research to predict the vibrational health risk level in conditions with no or missing data, as well. The root mean squire (RMS) of the vertical vibration of the seat and cabin floor was obtained during different operational conditions of an open pit mine in Iran. Then an artificial neural network was designed for the prediction of the vibrational health risk level. Regarding the results of this study, haul road quality, speed level, and load profile had a significant effect on vibration exposure. The average of the RMS values were 0.942 and 1.176 m/s2 for the good and poor road conditions, respectively that are in the high health risk levels. However, there was no significant relationship between the payloads, in the range of 20 to 30 tons, in the RMS values. At speeds higher than 30 km/h, the vibrational health risk was at high level for all conditions. Moreover, there were 93.83% correlation between the measured and simulated RMS values was found in the application of the neural network. This paper helps the mine managers to predict the unsafe conditions and consider the practical approach for the WBV risk reduction.

A Comparison of Multiple Machine Learning Algorithms to Predict Whole-Body Vibration Exposure of Dumper Operators in Iron Ore Mines in India

Background: This study deals with some factors that influence the exposure of whole-body vibration (WBV) of dumper operators in surface mines. The study also highlights the approach to improve the multivariate linear analysis outcomes when collinearity exists between certain factor pairs. Material and Methods: A total number of 130 vibration readings was taken from two adjacent surface iron ore mines. The frequency-weighted RMS acceleration was used for the WBV exposure assessment of the dumper operators. The factors considered in this study are age, weight, seat backrest height, awkward posture, the machine age, load tonnage, dumper speed and haul road condition. Four machine learning models were explored through the empirical training-testing approach. Results: The bootstrap linear regression model was found to be the best model based on performance and predictability when compared to multiple linear regression, LASSO regression, and decision tree. Results revealed that multiple factors influence WBV exposure. The significant factors are: weight of operators (regression coefficient β=-0.005, p<0.001), awkward posture (β=0.033, p<0.001), load tonnage (β=-0.026, p<0.05), dumper speed (β=0.008, p<0.001) and poor haul road condition (β=0.015, p<0.001). Conclusion: The bootstrap linear regression model produced efficient results for the dataset which was characterized by collinearity. WBV exposure is multifactorial. Regular monitoring of WBV exposure and corrective actions through appropriate prevention programs including the ergonomic design of the seat would increase the health and safety of operators.

SCAMPER Monitoring Platform to Measure PM10 Emission Rates from Unpaved Roads in Real-Time

The SCAMPER method for measuring PM10 emission rates from roadways was used to evaluate mitigation methods for public unpaved roads and a treated mine haul road. The SCAMPER method uses a small trailer to measure PM10 concentrations behind a vehicle at a point that is representative of the mean PM10 concentration in the vehicle’s wake. This concentration multiplied by the frontal area has been shown to be a reasonable estimate of the emission rate in units of grams per meter traveled. On public roads it was towed by a 2006 Ford Expedition and on a mine haul road it was towed behind both the Expedition and an earth mover weighing over 150 tons fully loaded. Since the SCAMPER is capable of measuring emission rates on both paved and unpaved roadways, a direct comparison of the effectiveness of mitigation methods with respect to a similar paved road was possible.

Detecting drainage pitfalls in open-pit mines and haul roads using UAV-photogrammetry

Open-pit mines generally have operational problems such as puddling and inappropriate water flow over haul roads, particularly if located in areas with high rainfall indices. These situations increase truck cycle times, promote rapid deterioration of haul-road wearing-course material, reduce productivity due to downtime and increase road maintenance. In addition, operational costs are raised as the frequency of truck maintenance and tire failures also increase. The use of a high-resolution three-dimensional elevation model, created based on Unmanned Aerial Vehicle (UAV) photogrammetry, has been shown to be an effective technique to detect anomalies in a fast and precise way. With the proposed approach, it is possible to diagnose haul-road conditions after rainfall or to anticipate the potential occurrence of such anomalies before they become a greater problem. This diagnosis can then be used to prioritize maintenance activities in open-pit mines. To describe the methodology, a case study is presented demonstrating and validating the results obtained.

Evolutionary computation and genetic algorithms for designing the optimal mine haul road pavements

An overview of the software for designing flexible road pavements used in the USA, European countries and the Russian Federation is given, conceptual and mathematical models for designing flexible road pavements with the minimum cost per a linear meter are presented, the application of evolutionary algorithms in combination with exhaustive search and parallel computing is substantiated, the developed hybrid genetic algorithm for the optimization of flexible road pavement designs in terms of value of materials of layers is described, the results of a simulation experiment performed using the software developed within the framework of the study are presented in the article. It is proposed to use the developed software and algorithms for designing the optimal haul road pavements in terms of material cost.