Minimising Total Costs across a network of Roads
Various road maintenance methods can be applied depending on the type of mining and complexity of the operation. Ideally however, road-user costs need to be minimised and road performance maximised, and a systematic approach to road maintenance management is best. This is referred to as a Maintenance Management System (MMS) and through an analysis of how quickly roads deteriorate under traffic action, how this affects vehicle operating costs and how much it costs to maintain the road (both costs being carried by the mine), an optimum maintenance frequency is found.
Using an ad-hoc or even a routine-based maintenance management system will not deliver minimum total costs. The concept is shown in the figure, where total road-user costs comprise vehicle operating costs (VOC) and road maintenance equipment and application costs. The key to minimising total cost across a network of road segments in a mine is to determine which segments have the greatest influence on cost per ton hauled. It is these segments (invariably ramp or long flat high traffic volume hauls) that should enjoy priority in any maintenance system - since a small reduction in rolling resistance will have the greatest influence on reducing cost per ton hauled across the network. Mine haul road maintenance intervals are closely associated with traffic volumes, operators electing to forgo maintenance on some sections of a road network in favor of others. This implies an implicit recognition of the need to optimize limited road maintenance resources to provide the greatest overall benefit. This optimization approach is inherent in the structure of the maintenance management system (MMS) for mine haul roads. Two elements form the basis of the economic evaluation, namely:
■ pavement functional performance - rolling resistance model of
deterioration; and
■ vehicle operating and road maintenance cost models.
MMS is designed for a network of mine haul roads, as opposed to a single road analysis. For a number of road segments of differing wearing course
■ the change in road rolling resistance with time and traffic;
■ the maintenance quantities as required by the particular maintenance strategy;
■ the vehicle operating and road maintenance costs; and
■ the optimal maintenance frequency for segments of the network such that total road-user costs are minimised.
This approach is represented in the flow-chart. Cost savings associated with the adoption of a maintenance management system approach are dependant on the particular hauling operation, vehicle types, road geometry and
tonnages hauled, etc.
The model can accommodate various combinations of traffic volumes, road segment geometries and wearing course material properties to enable a full road network simulation to be completed.
Vehicle operating Cost and Rolling Resistance in MMs
The first element of an MMS for mine haul roads is based on modeling the variation of vehicle operating costs with rolling resistance. When combined with a road maintenance cost model, the optimal maintenance strategy for a specific network of haul roads, commensurate with lowest overall vehicle and road maintenance costs, may be identified.
Vehicle operating cost (VoC) model
The vehicle operating cost model refers to the incremental cost of truck operation with changes in road rolling resistance. The cost model should consider the effect of increasing rolling resistance on fuel consumption, tyres and vehicle wear and repair. However, a reasonable approximation can be determined from fuel consumption alone. The prediction of fuel consumption variation with rolling resistance involves simulation with specific haul trucks to generate a speed model for various road grades. The speed model forms the basis of the fuel consumption model, derived from vehicle simulations coupled with vehicle engine torque (or percentage full throttle) and fuel consumption maps.
Road maintenance cost model
The road maintenance operating cost per kilometer comprises both grader and water car operating costs. Although not contributing directly to a reduction in rolling resistance, the incorporation of the watering costs in the maintenance costs model reflects (the ideal) operating practice in which, immediately before blading, the section of road is watered to reduce dust, erosion and aid blading and recompaction.
Grader and water-car productivities of 0.75 and 6.25 km maintained road per operating hour for each machine, respectively, is typical and correlates
achieve an acceptable finish when road ‘roughness’ exceeds approximately 3% rolling resistance (RR%)) increases. The road maintenance cost model is thus constructed from consideration of the average blade width per pass, road width, RR% before blading, motor-grader productivity curve and hourly cost from which the motor-grader cost per kilometer is found. This cost is then combined with the cost per kilometer of the water-car and other costs to produce a total cost per kilometer for road maintenance.
example of MMs application
Using an example to illustrate the use of MMS, applied to a typical surface mine haul road network. The generic data is shown here.
Data specific to each of 5 segments of a mine haul road network are shown below. A segment is defined where one or more of the model parameters vary, resulting in a slightly different cost structure or road segment
performance. note in the table, a significant number of the values used are
out of specification. As will be seen, once the cost of under-performance is established, management action can be prioritised to bring the most critical roads back to specification by rehabilitation.
MMS Model: Generic data for all haul road segments
Truck GVM (t) 324 Truck UVM (t) 147 Drive type 1 Replacement cost (Rm) 19 Average age (khrs) 40 Grader fleet 6 Grading hours/day 19 Grader Op cost (R/hr) 370
Water car fleet 3
Watering hours/day 19 Water car op cost (R/hr) 510
Tyre cost (R) 204 000
Fuel cost (R/l) 6,26
Segments specific data B02 B03 B04 B05 S RAMP
Road length (km) 2600 2300 1800 1100 1240
Width (m) 35 35 35 35 40
Grade (%, uphill +ve) 0 0 0 0 4.5
Speed (km/h) 45 40 35 30 25
Daily tonnage (kt) 20.4 20.4 25.5 30.6 100.3
Material type (1=mix) 1 0 0 0 1
Shrinkage product 189 243 243 243 180 Grading coefficient 20 15 15 15 28 Dust ratio 0.57 0.50 0.50 0.50 0.64 Plasticity index 8 14 14 14 8 CBR (%) 100% Mod AASHTO 44 38 38 38 59
By modelling the rate of change in rolling resistance with time (i.e. traffic volumes) for each of the road segments, the lowest total road user cost can be found. Only when the combination of road maintenance and VOC are combined - the ‘total road-user costs’ - can we determine the most efficient approach to road maintenance.
The Figure shows the cost penalty (as percentage change in total road-user costs) associated with under- or over-maintenance of segments. Note how total road-user costs increase when segments B03-B05 are over-maintained - you should only maintain these segments at a three-day interval - more frequent than that and you are incurring excessive road maintenance costs. Note also how road-user costs increase when segment SRamp is not maintained every day - by delaying maintenance on the SRamp to every 2 days, a 5% cost penalty is immediately incurred.
The Figure shows the importance of establishing road performance characteristics as a basis for road maintenance management decisions - in this case, if grader availability was low, it would make more economic sense to forego maintenance on the B03-B04 segments since the cost penalty associated with sub-optimal maintenance is much lower for these segments. Cost savings associated with the adoption of a maintenance management
What is, however, generic to any analysis of MMS for a network of roads is that to reduce costs across the board, road performance needs to be maximised. This is best achieved through an integrated design approach, where
geometric, structural and functional design components contribute to a road that has only a slow rate of deterioration, hence rolling resistance (and thus VOC) do not increase substantially and maintenance intervals can be extended without significant cost penalties.