J. Cent. South Univ. (2017) 24: 1808-1816

DOI: https://doi.org/10.1007/s11771-017-3589-3

Health risk of whole body vibration in mining trucks during various operational conditions

Rahimdel M.J¹, Mirzaei M², Sattarvand J¹, Hoseinie S.H³

1. Department of Mining Engineering, Sahand University of Technology, Tabriz 5331711111, Iran;

2. Department of Mechanical Engineering, Sahand University of Technology, Tabriz 5331711111, Iran;

3. Department of Mining Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran

Central South University Press and Springer-Verlag GmbH Germany 2017

Abstract: Mining machineries are generally exposed to intensive vibrations in harsh mining environment. If vibrations are beyond the tolerable limit, the machine and its operator health will be under the risk. In this work, the vibration of a mining truck at different operational conditions are simulated and discussed. To achieve this aim, three haul roads with low, medium and poor qualities are considered based on the ISO standard. Accordingly, the vibration of a mining truck in different speeds, payload and distribution qualities of materials in the dump body are evaluated in each haul road quality using Trucksim software. The simulation results with statistical discussions indicate that the truck speed and the materials distribution quality have significant effects on the root mean square (RMS) of vertical vibrations. However, the effect of the payload is not considerable on the RMS. Moreover, the accumulation of materials on the rear side of the truck dump body is efficient on the vibrational health risk.

Key words: mining trucks; operational conditions; health risk; whole body vibration

1 Introduction

Operators of industrial vehicles are mainly exposed to the dangerous levels of whole-body vibrations (WBV). These vibrations reduce the efficiency of operators and have undesirable effects on their health and safety. Daily exposures to WBV over a number of years can result in serious physical damage and affect the exposed person circulatory and/or urological systems [1]. Therefore, evaluation and reduction of the harmful WBV in vehicles have been the main goal of many researches [2, 3].

All of mining machineries and equipment produce a lot of vibrations during their operations. Moreover, these equipments are exposed to violent vibration of harsh mining environments. Many researchers in mining industries have already analyzed the health risk level of WBV for different vehicles such as haul trucks [4-7], load haul dump (LHD) [8, 9], shovels [10] and drilling machines [11, 12]. Among the mining vehicles, mining trucks usually operate at various work cycles including loading, transporting, dumping the load and returning to the loading aria and also in different haul road conditions. Other vehicles are fixed or have a very short movement in their working place. Therefore, mining trucks are in more dangerous vibrational health risk than the other vehicles.

KUMAR [4] studied the WBV of two types of trucks: 240 t mechanical drive trucks and 320 t electric drive trucks using field data analysis. In this work, the data were collected on smooth frozen roads. The results revealed that the vibrational health risk is greatest when trucks are driven unloaded. Also, it was found that WBV health risk was in less exposure level on smooth roads. EGER et al [13] measured the WBV of two 150 t haul trucks using field data. The average of the root mean square (RMS) of vertical acceleration was recorded between 0.28 and 0.37 m/s² for two trucks which indicates the low health risk level. SMETS et al [5] measured WBV of eight haulage trucks with 35, 100, 150 t capacities using the field data. The results revealed that the truck operators were typically exposed to moderate through high level of WBV risk. Also, the truck type has no significant effect on the RMS of vertical vibrations which was in consistent with Kumar’s data. In this research the interaction between speed, path selection, road roughness and the vibration magnitude and their effect on the WBV were not discussed. FRIMPONG et al [6] studied vibrations in high-impact shovel loading operations (HISLO) transmitted into cabin using a 3D dynamic model in MSC.ADAMS software. The results of this research showed that the maximum vibrations in the loading period occur at the first and the second passes, when the truck is empty.Therefore, they proposed that the vibration control in the first two passes of loading operation is important. In a recent study, the WBV exposure of 32 haul truck was measured [14]. The RMS of vertical acceleration for 12 trucks was in low health risk level. However, the truck size and haul road quality had no significant effect on the RMS. Also, haul road conditions considered by field observations had a large effect on the vertical vibration.

In the most researches reviewed above, the effect of truck type, driver gender, work cycle and loaded or unloaded truck dump body on the WBV has been investigated. But, there is no enough research considering the various mining operational conditions such as truck speed, payload and distribution quality of materials in truck dump body. These parameters can influence the vibration of small to medium mining trucks because of their light weights. In this work, in order to compensate this scarcity, an extensive range of operational conditions and their effects on the WBV of mining truck are studied. These operational conditions include the various haul road qualities, truck speeds and payloads. Also, distribution qualities of materials in the dump body including uniformly distributed materials or accumulated materials on the right, left, front and rear sides of the truck dump body are considered. Vibrational simulation is carried out using Trucksim software and the results are illustrated and analyzed statistically.

The results of this work provide guiding principles for the operators to drive the vehicle with a low risk. Also, this work opens a new window for the future researches and designers to present the practical solutions for the health risk reduction of mining trucks. Optimization of the vehicle suspension system, maintenance of haul road, improvement of loading quality and suggestion for the safe speed limits based on the haul roads quality can reduce the health risk of operators.

2 Whole body vibration analysis

Many standards have been introduced to WBV measurement and analysis. The most popular standard for measurement and evaluation of the human response to WBV is ISO 2631-1. Two main criteria for describing acceleration amplitude in the ISO 2631-1 are frequency-weighted RMS (a_wrms) and vibration dose value (V_DV) which are defined as follows [15]:

                        (1)

                           (2)

where T is the measurement duration and a_w(t) is the frequency-weighted acceleration at time t. Accordingly, the daily vibration exposure for 8 h equivalent frequency-weighted RMS is calculated as [15]

                             (3)

where a_w is the frequency-weighted RMS and T is the exposure time in hour.

To evaluate the health risk level of vibrations, ISO 2631-1 defines “hEALTH gUIDANCE cAUTION zONE, (HGCZ)” according to Fig. 1. In practice, the exposures below, within and above the HGCZ are usually considered low, moderate and high health risk, respectively.

Fig. 1 Health guidance caution zones provided in ISO2631-1 Annex B [15]

For an 8 h daily exposure, the upper and lower bounds of HGCZ are 0.47 m/s² and 0.93 m/s², respectively, based on the RMS. The corresponding values for the V_DV measure are 8.5 m/s^1.75 and 17 m/s^1.75 [16]. Also, ISO 2631-1 defines the Crest Factor (F_C) as the ratio of the maximum instantaneous peak value of the a_w(t) to its RMS value [15],

                            (4)

According to ISO 2631-1, if the F_C exceeds 9, vibration effects on the driver’s body may be not estimated. In this condition, the V_DV is used for evaluation and prediction of health risk.

3 Mechanical simulation of vibrations

Computer simulations have increased during the past decades as a powerful scientific tool for systems. In this work, TruckSim as a powerful software for simulating the behaviour of heavy trucks is used for simulation studies. A three-axle truck shown in Fig. 2 with the parameters of Table 1 is used for the simulation study. Such trucks are the popular mining trucks due to their high maneuverability and compatibility in the various operational conditions. Operational conditions are described at the following sub-section.

Fig. 2 Truck used for simulation study

Table 1 Parameters of case study truck

The operational conditions for the simulation studies are given in Table 2. The truck speed is in the range of 40 to 70 km/h, with 5 km/h intervals. The payload is 24 to 30 t, with 2 t intervals. To consider materials distribution qualities, the materials gravity center is moved around the aria center of the truck dump body. Also, the uniformly distributed materials are considered.

Table 2 Operational conditions for simulated truck

In this work, for definition of the haul road quality, road classification based on the ISO standard is used. In this classification, road roughness has been classified using the power spectral density (PSD) values at the special frequency of 1/(2π) cycle/m. In ISO classification, the relationship between the PSD and the special frequency on logarithmic scale is considered for the road classification as Fig. 3. This relationship for the different road classes is approximated by two straight lines as follows [17]:

     (5)

     (6)

where S_g is the PSD, Ω is the spatial frequency and N₁ and N₂ are 2 and 1.5, respectively. Ranges of S_g(Ω₀) values at the special frequency Ω₀= 1/2π cycle/m for the different class of road are given in Table 3. In this work, as shown in Fig. 4, three roads in the classes of good (B), medium (C) and poor (D) are created based on the ISO classification for the truck constant speed of 65 km/h.

Fig. 3 Classification of road roughness by ISO [17]

Table 3 Classification of road roughness in ISO [17]

4 Simulation results and discussions

In this section, simulation studies are carried out to show the health risk level of the case study truck vibration at the various operational conditions. Regarding all states defined in Table 2 together with three haul road qualities, the simulated truck is run in all 1092 (7×4×13×3) operational conditions. The 8 h equivalent frequency-weighted RMS (A(8)) of the driver which is in the left side of the truck cabin at all 1092 conditions is obtained. It is noted that, in each trial, it is assumed that the truck operates only 7 h in each 8 h working cycles. For example, Fig. 5 represents the vibration signal in time and frequency domainscorresponding to 30 t uniformly distributed materials at good haul road condition and speed 40 km/h.

Fig. 4 Three roads created in good, medium and poor classes

Fig. 5 Vibration signal for 30 t uniformly distributed materials at good quality haul road in time and frequency domains (speed 40 km/h)

According to Fig. 5, the RMS of vertical vibration is 0.642 m/s² which indicates the moderate health risk. Also, the high vibrational energy is observed at 2.21 Hz in the frequency domain. For the same condition, the RMS values of signal for the medium and poor quality haul roads are calculated as 0.793 m/s² and 1.074 m/s², respectively. The results indicate that the deterioration of the haul road quality from good to medium and poor, respectively, increasing the RMS values by 23.52% and 67.29%. Also, the RMS of vibration increases by 26.11% on average with the change of the road quality from medium to poor. At the good quality haul road, if the speed increases from 40 km/h to 45 and 50 km/h, the RMS increases by 11.21% and 22.59%, respectively. It can be concluded that if the materials are distributed normally, the effect of road quality on the RMS is higher than the effect of the truck speed.

4.1 Vibrational health rick in various operational conditions

It is clear from the previous results that the vibrations are changed under various operational conditions. Therefore, in this section, effects of the operational conditions on the RMS are studied. The RMS of vibrations for 30 t materials accumulated on different sides of the dump body is shown in Fig. 6. According to Fig 6(a), when the materials are accumulated on the left side (driver side) of the truck dump body, the RMS values of vertical acceleration of the driver in all haul road conditions are higher than the other materials distribution qualities. It is obvious from Fig. 6 that almost all of the cases in good quality haul road are in the medium risk level. In the good quality haul road, increasing the speed has no considerable effect on the health risk level.

By changing the haul road quality from good to medium and poor, the effect of speed when the materials are accumulated on the left side is high. In this case, when the truck speed is more than 50 km/h, the speed effect on RMS will be more. It is concluded that in the poor and medium haul road conditions, the reduction of speed and also the avoidance of the materials accumulation on the driver side are effective to health risk reduction.

From the above discussion, it is found that the accumulation side of materials at the truck dump body is one of the most effective parameters on the RMS values, especially at the poor haul road conditions. Now, the effect of materials distribution quality on the RMS at the various speed levels is investigated. Figure 7 indicates that at speeds of 40 and 50 km/h, almost all conditions with good and medium quality haul roads are in the HGCZ area and the materials distribution qualities have no remarkable effect on the RMS values. With increasing the truck speed to the high values (60 or 70 km/h), the RMS of vibrations increases. These effects become more and more at the poor haul road quality. As a result, at the high speed values (higher than 50 km/h), either the truck speeds or the materials distribution qualities have considerable effects on the RMS values. Also, accumulation of materials on the left side of the dump body has the highest effect on the RMS values.

Fig. 6 A(8) for 30 t materials accumulated on left (a), right (b), front (c) and rear (d) sides as 300 mm

Fig. 7 A(8) for different materials distribution qualities at speeds 40 (a), 50 (b), 60 (c) and (d) 70 km/h (Payload: 30 t)

In what follows, the effects of payload changes on the RMS values are studied. The RMS of vibrations for different payload at the speed of 40 km/h is shown in Fig. 8. As it is seen, the change of payload has no remarkable effect on the RMS. Changing the payload from 24 t to 30 t, decreases the RMS of vibration by only 5% on average.

4.2 Comparison analysis of mean values of RMS

The previous results revealed that the vibrational health risk is dependent on the haul road quality, speed and also the distribution quality of materials in the truck dump body. In this section, to have a comprehensive assessment of the operational conditions effect on the health risk, the mean comparison analysis is carried out. Figure 9 shows the mean RMS values at the various operational conditions as the independent data sets. According to the mean analysis, only 19.54% of all conditions are in the moderate health risk level for the case study truck and almost all of them are in the good quality haul road condition. The other cases are in the high health risk. It should be noted that the accumulation of materials in rear side of the dump body has no remarkable effect on the mean RMS values. This is due to the uniformly distribution of truck gross weight among the tires. Therefore, to create the health and safety loading condition, it is proposed that the loader operators should try to load the materials, uniformly or cumulating them on the rear side of the dump body, as much as possible. With accumulation of materials on the rear side of the dump body, not only the vibrational health risk will be reduced, but also dumping of the materials will be done easily and safety.

4.3 Significant association between operational conditions and the RMS values

In this section, the mean RMS values are compared in the different operational conditions data sets and the effects of each data set on the mean RMS are statistically analyzed. To achieve this aim, analysis of the variance (ANOVA), is used. ANOVA is used to determine the effect of independent variable(s) on the dependent variables. In ANOVA, the absence of a difference between the independent variable(s) and the dependent variables is defined as the null hypothesis. The p value is used to accept the null hypothesis. Small p values, typically smaller than 0.05, indicate that there is a strong evidence to reject the null hypothesis. Large p values (greater than 0.05) indicate that the null hypothesis is acceptable. In this work, the multivariate analysis of variance is done at 5% significant level using SPSS.9 software and the results are given in Table 4. According to Table 4, there is a significant difference between the haul road qualities, the truck speeds and the materials distribution qualities in the RMS values (p< 0.001). Also, there is no significant difference between payloads (p> 0.05) in the RMS values. On the other hand, the road conditions, the truck speeds and the materials distribution qualities have the most effect on the RMS values. While, the payload has no significant effect on the RMS. Other two-way and three-way interactions of the operational conditions are presented in Table 4 which have the significant effect on the RMS values.

Fig. 8 A(8) vs payloads for materials accumulated as 300 mm on right (a), rear (b), front (c) and left (d) at speed of 40 km/h

Fig. 9 Comparisons of mean RMS values at various operational conditions

At the remaining of this section, the effect of various haul road qualities, the truck speed values and also the distribution qualities of materials on the RMS values is studied. To achieve this aim, Scheffe’s post hoc test is used. The Scheffe’s post hoc test is the mean comparison test used for finding relationships between the sub-groups of the significant parameters. Results of the Scheffe test are given in Tables 5 and 6. In this table,there is no significant difference between each subgroups of the operational conditions which have the same symbol (p>0.05). Scheffe test reveals that there is a significant difference between all haul road qualities. Poor and good haul roads, respectively, have the highest and lowest effect on the RMS values. The payload levels have no significant effect on the RMS values (with the same symbol). Moreover, the accumulation of the materials on the rear sides has the lowest effect on the RMS. It should be noted that the uniformly distributed materials has the middle effect on the RMS values.

Table 4 Results of ANOVA at 5% significant level

Table 5 Results of Scheffe’s post hoc test

Table 6 Material accumulation side

5 Conclusions

In this work, vibrational health risk of a truck at the various mining operational conditions was analyzed statistically. Haul road quality, truck speed, payload and distribution quality of the materials in the truck dump body are considered as the main operational conditions. The RMS of vertical acceleration at the left (driver) side of the truck cabin is obtained using Trucksim software at the all operational conditions and evaluated according to ISO 2631-1.

Results show that the mining truck drivers are exposed to moderate to high vibrational health risk. The haul road quality, the truck speed and the materials distribution quality in tuck dump body are the effective parameters on the health risk. While, the payload has no significant effect on the RMS values. If the materials are uniformly distributed, the effect of the haul road quality on the health risk is higher than the effect of the truck speed. In the poor quality haul roads, at the low speed levels (<50 km/h), the material distribution quality has no remarkable effect on the vibrational health risk. At speed above 50 km/h, accumulation of the materials on the left and rear side of the dump body, respectively, has the highest and the lowest effect on the vibrational health risk.

To reduce the vibrational health risk, it is proposed that the loader operators should attempt to distribute the materials, uniformly or accumulated them near to the rear side of the dump body, as much as possible. Also, persuasion of the drivers to reduce the truck speed at the poor haul road conditions to at least 50 km/h is recommended. These results provide guiding principles for the operators to drive the mining trucks with a low risk.

References

[1] MANSFIELD N J. Human response to vibration [M]. New York: CRC Press, 2004.

[2] MIRZAEI M, HASSANNEJAD R. Application of genetic algorithms to optimum design of elasto-damping elements of a half-car model under random road excitations [J]. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 2007, 221(4): 515-526.

[3] KHIAVI A M, MIRZAEI M, HAJIMOHAMMADI S. A new optimal control law for semi-active suspension system considering nonlinear magneto-rheological damper model [J]. Journal of Vibration and Control, 2014, 20(14): 2221-2233.

[4] KUMAR S. Vibration in operating heavy haul trucks in overburden mining [J]. Appl Ergon, 2004, 35(6): 509-520.

[5] SMETS M P H, EGER T R, GREINER S G. Whole-body vibration experienced by haulage truck operators in surface mining operations: A comparison of various analysis methods utilized in the prediction of health risks [J]. Appl Ergon, 2010, 41(6): 763-770.

[6] FRIMPONG S, GALECKI G, CHANG Z. Dump truck operator vibration control in high-impact shovel loading operations [J]. International Journal of Mining, Reclamation and Environment, 2011, 25(3): 213-225.

[7] SHEN Y H, XU M, JIN C, GAO Y, WEI F L. Operator health risk evaluation of off-highway dump truck under shovel loading condition [J]. Journal of Central South University, 2015, 22: 2655-2664.

[8] EGER T, STEVENSON J, CALLAGHAN J P, GRENIER S. Predictions of health risks associated with the operation of load-haul-dump mining vehicles: Part 2—Evaluation of operator driving postures and associated postural loading [J]. International Journal of Industrial Ergonomics, 2008, 38(9): 801-815.

[9] EGER T, STEVENSON J M, GRENIER S, BOILEAU P , SMETS M P. Influence of vehicle size, haulage capacity and ride control on vibration exposure and pre-dicted health risks for LHD vehicle operators [J]. Journal of Low Frequency Noise, Vibration & Active Control, 2011, 30(1): 45-62.

[10] MANDAL B B, PAL A K, SISHODIYA P K. Vibration characteristics of mining equipment used in Indian mines and their vibration hazard potential [J]. International Journal of Environmental Health Engineering, 2013, 2(1): 45: 1-9.

[11] CHAUDHARY D K, BHATTACHERJEE A, PATRA A. Analysis of whole-body vibration exposure of drill machine operators in open pit iron ore mines [J]. Procedia Earth and Planetary Science, 2015, 11: 524-530.

[12] CHAUDHARY D K, BHATTACHERJEE A, PATRA A K, CHAU N. Whole-body vibration exposure of drill operators in iron ore mines and role of machine-related, individual, and rock-related factors [J]. Safety and Health at Work. 2015, 6(4): 268-278.

[13] EGER T, SALMONI A, CANN A, JACK R. Whole-body vibration exposure experienced by mining equipment operators [J]. Occup Ergon, 2006, 6(3): 121-127.

[14] WOLFGANG R, BURGESS-LIMERICK R. Whole-body vibration exposure of haul truck drivers at a surface coal mine [J]. Applied Ergonomics, 2014, 45(6): 1700-1704.

[15] ISO 2631-1. Mechanical vibration and shock: Evaluation of human exposure to whole-body vibration. Part 1, General Requirements, 1997 [R].

[16] GARY B M F, ARDRIE L. Bad vibrations: A handbook on whole body vibration exposure in mining [M]. 2001.

[17] WONG J Y. Theory of ground vehicles [M]. New York: John Wiley & Sons, 2001.

(Edited by DENG Lü-xiang)

Cite this article as: Rahimdel M.J, Mirzaei M, Sattarvand J, Hoseinie S.H. Health risk of whole body vibration in mining trucks during various operational conditions [J]. Journal of Central South University, 2017, 24(8): 1808-1816. DOI: https://doi.org/10.1007/s11771-017-3589-3.

Received date: 2016-03-28; Accepted date: 2017-01-13

Corresponding author: Mirzaei M, PhD, Associate Professor; Tel: +984113459474; E-mail: mirzaei@sut.ac.ir