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And hope you remember these curves are valid only for these ideal conditions.

This statement prompts us to understand that the production curves developed for machinery (like bulldozers) are based on specific ideal conditions. These conditions might include factors like soil density, soil type, operator skill, and environmental conditions. Therefore, if any of these factors deviate from the ideal, the productivity represented by these curves may not hold true.

Think of a recipe for a cake that turns out perfectly if you follow it precisely, using specific measurements and ingredients. If you were to change the type of sugar or add too much flour, the cake may not rise and taste as intended. Similarly, if the conditions for a bulldozer's operation are altered, the productivity might drop.

So, you are supposed to apply the job efficiency, you have to apply the correction factor accordingly.

When using machinery like bulldozers, it is important to account for real-world efficiencies. For example, if a bulldozer is designed to work for 60 minutes but can only operate effectively for 50 minutes due to various interruptions, we need to adjust productivity predictions downwards. This adjustment is done with a correction factor that reflects job efficiency.

Imagine a student who can normally study effectively for an hour but is only focused for 45 minutes on a particular topic. If their study material suggests they can learn four concepts in 60 minutes, in reality, they might learn three concepts in that same time frame. The adjustment (or correction) reflects their actual performance.

But this curve value is applicable for a soil density of 1365 kg per meter cube. So, in our case the soil density is given as 1750 kg per meter cube in bank state that is to be noted.

The production curves used for bulldozers are based on certain assumptions, including soil density. If the soil density in a particular project is higher than what the curve was based on, this can lead to less productivity than expected. In this case, the curve is based on looser soil (1365 kg/m³) while the actual soil is denser (1750 kg/m³), indicating additional effort is required to move the soil.

Consider trying to push a shopping cart filled with light groceries versus one filled with heavy items. The heavier cart requires more effort and time to move the same distance. This difference in 'weight' corresponds to the soil densities discussed.

Then other things like operator the curve was drawn for excellent operator skill. So, in our problem the operator skill is average.

Another factor that affects productivity is the skill level of the operator. If the production curve assumes that an 'excellent' operator is at the controls but a less skilled operator is actually using the machine, this may lead to a decrease in expected output. Therefore, a correction factor must be applied when the operator's skills do not meet the ideal scenario.

Think of a musician performing a song. A skilled musician may play a challenging piece flawlessly, while a beginner might struggle and take longer to finish. If you assumed a skilled performance, but instead had a beginner, you would need to revise your expectations.

Similarly, the material type, material type is non-cohesive silty sand, so that will definitely reduce the productivity.

The type of soil or material being worked with also significantly influences productivity. Non-cohesive materials like sandy soil, while easier sometimes to push, may not compact well, leading to inefficiencies during earthmoving. This means adjustments need to be made when determining expected output.

If you're using a shovel to dig in sand compared to digging in clay, the effort and technique required differ. Sand might flow easily but can collapse, while clay is harder and slower to dig through. Each material presents its own challenges.

Visibility is poor in the problem what we have discussed. So, that will reduce your correction factor, the productivity will reduce obviously.

Working conditions like visibility also play a crucial role in how effectively machines can operate. Poor visibility can lead to errors, slow operations, and decreased productivity, requiring lower productivity estimates based on these conditions.

Imagine trying to drive a car during a heavy rainstorm. You would likely drive more slowly and cautiously because of reduced visibility, affecting how quickly you reach your destination. Machines operate similarly under poor visibility.

So, in this problem we are moving down the hill, so the grade percentage is -15%. So, accordingly you have to choose the curve supply by the manufacturer.

The slope of the terrain can either aid or hinder productivity. If the bulldozer is working downhill (-15% grade), gravity can assist in moving the soil, potentially increasing productivity. Yet, accurate adjustments must still be made based on actual conditions against manufacturer curves.

Consider walking downhill versus walking uphill; going downhill is typically easier and faster, due to gravity helping you. This analogy reflects how machinery operates on different slopes.

So, after you estimate the productivity you are asked to estimate the unit cost of proposed earthmoving operation per bank meter cube.

Estimating costs is crucial for project planning. The unit cost of an earthmoving operation per bank meter cube can be calculated by dividing the total hourly operating cost of the machinery by its estimated productivity (in bank meter cubes). This helps in budgeting and bidding processes.

Think about buying pizza; if one pizza costs $10 and can feed 2 people, then the cost per person is$5. Similarly, knowing the cost of operating machinery per unit of output helps businesses plan effectively.

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