3.2. Cycle Time Calculation

And hope you remember these curves are valid only for these ideal conditions. So, 60 minutes hour but in your project in this problem we found that the machine is working for 50 minutes an hour.

This part highlights that the productivity curves used in calculations depend on ideal conditions. Here, one ideal condition is that the machine operates for an entire hour (60 minutes). However, in the current project scenario, the machine only works efficiently for 50 minutes per hour. This difference is crucial because it will affect the final productivity results due to reduced active work time.

Imagine a student who can only study effectively for 50 minutes before losing focus, even though a full study session is an hour long. Just like the student’s effective study time needs to be considered for proper planning, the machine's working time must be adjusted to calculate productivity accurately.

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

Given that the machine operates less than the ideal amount of time, we need to apply a correction factor to account for decreased efficiency. This means adjusting the expected productivity from the ideal curves based on real-world conditions such as operating time and other factors like soil density and operator skill.

Think of a chef preparing a meal. If they work in an understocked kitchen (like working under less than ideal conditions), the meal's preparation and cooking time increase. They might need to adjust their plan considering their limited resources, just like adjusting the productivity expectations based on working conditions.

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 us 1750 kg per meter cube in bank state that is to be noted.

The productivity curve that was initially used assumes a specific soil density of 1365 kg/m³. However, in our case, the soil density is actually higher at 1750 kg/m³. This discrepancy implies that the bulldozer will face more resistance when working with denser soil, which will reduce its productivity. Therefore, this requires us to apply another correction factor to adjust for this difference.

Imagine pulling a wagon filled with rocks versus one filled with feathers. The rock-filled wagon represents denser soil, which requires more effort to move. Just as the increased weight affects how fast you can pull the wagon, the denser soil affects bulldozer productivity.

The curve was drawn for excellent operator skill. So, in our problem the operator skill is average. So, accordingly you have to choose the correction factor and apply.

Since the ideal productivity curves assume an 'excellent operator' handling the machinery, the actual average skill level of the operator requires a correction. An average operator will likely work less efficiently than an expert, necessitating a decrease in expected productivity through a correction factor.

Think of a sporting event. If a professional athlete (excellent operator) competes, the expectations are high. However, if an amateur (average operator) competes instead, you would expect less impressive performance, similar to how the bulldozer's output changes based on operator proficiency.

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

The type of material being moved greatly affects productivity. Non-cohesive silty sand does not hold together well, which can influence how the bulldozer operates. This material's properties may slow down operations or require different techniques, further necessitating correction.

Think about trying to move a pile of dry leaves versus a pile of water-soaked mud. The mud is heavier, stickier, and much harder to move. Like in construction, the bulldozer's efficiency is impacted by the nature of the material it is handling.

Visibility is poor in the problem what we have discussed. So, that will reduce your correction factor, the productivity will reduce obviously we are working for 50 minutes an hour, so accordingly the correction factor should be applied.

Poor visibility conditions can hinder the operator's ability to navigate and control the bulldozer effectively. This can reduce productivity, requiring a correction factor to account for reduced efficiency during operations in such conditions.

Consider driving in foggy weather; it is more challenging to see the road. Consequently, you might drive slower to stay safe. Similarly, reduced visibility affects the bulldozer's efficiency, making it crucial to adjust productivity expectations.

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.

Adjusting for the grade percentage—here indicated as -15% (downhill)—can actually enhance productivity. The slope contributes to the machine's effectiveness as gravity assists the bulldozer in moving material down the slope, thus necessitating another adjustment to the correction factor to reflect this advantage.

Think of riding a bike downhill versus uphill. Riding downhill feels easier and faster due to gravity helping you. In construction, moving down a slope similarly boosts the bulldozer's efficiency, showing how changes in terrain can impact operations.

So, we can compare both only when the volume is the same type of measure, but one volume is in bank state other volume is expressed in loosen state.

To accurately apply the soil density correction, we must convert the bank state soil density (1750 kg/m³) to a loose state density value to compare it with the productivity curve's standard (1365 kg/m³). This ensures we understand how denser soil impacts machine performance and productivity calculations.

When you weigh a bag of flour at the grocery store, the weight can vary depending on whether the flour is compacted or loose. In construction, just as measuring flour correctly is critical for a recipe, accurately comparing soil densities is vital for determining bulldozer productivity.

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

Once productivity is estimated, the next step is calculating the cost per unit of work done (in bank meter cube terms). This involves dividing the total cost of machine operation by the estimated productivity to understand the financial aspect of the earthmoving task.

It's like budgeting for a homemade pizza. If the total cost of ingredients is $20 and you can make 4 pizzas, the cost per pizza is$5. Similarly, here it’s crucial to know how much each unit of earthmoving costs to plan effectively.

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