Analyzing Construction Progress Data
Tracking construction progress is essential for successful project management in civil engineering. By collecting data such as daily work quantities or completion percentages, you can monitor how a project advances relative to its schedule. This data-driven approach helps ensure that construction milestones are met and resources are efficiently allocated.
123456789101112# Daily completed work quantities in cubic meters daily_work = [10, 12, 8, 15, 11, 13, 9, 14, 10, 12] # Calculate cumulative progress cumulative_progress = [] total = 0 for qty in daily_work: total += qty cumulative_progress.append(total) print("Daily completed work:", daily_work) print("Cumulative progress:", cumulative_progress)
Cumulative progress shows the total amount of work completed up to each day, providing a clear picture of overall project advancement. By reviewing cumulative progress, you can quickly assess whether the project is on track, ahead, or behind schedule. This information is crucial for identifying potential delays early, allowing you to take corrective action before issues escalate.
12345678910111213141516171819202122import matplotlib.pyplot as plt # Sample data days = list(range(1, 11)) cumulative_progress = [10, 22, 30, 45, 56, 69, 78, 92, 102, 114] milestone_days = [4, 8] milestone_values = [cumulative_progress[3], cumulative_progress[7]] plt.figure(figsize=(8, 4)) plt.plot(days, cumulative_progress, marker='o', label='Cumulative Progress') plt.xlabel("Day") plt.ylabel("Cumulative Work (m³)") plt.title("Construction Progress Over Time") plt.grid(True) # Annotate milestones for day, value in zip(milestone_days, milestone_values): plt.annotate(f"Milestone\nDay {day}", (day, value), textcoords="offset points", xytext=(0,10), ha='center', color='red') plt.legend() plt.tight_layout() plt.show()
1. What is the benefit of plotting cumulative progress in construction projects?
2. How can Python help identify project delays from progress data?
3. Fill in the blank: The ______ function in matplotlib is used to add text annotations to a plot.
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Tracking construction progress is essential for successful project management in civil engineering. By collecting data such as daily work quantities or completion percentages, you can monitor how a project advances relative to its schedule. This data-driven approach helps ensure that construction milestones are met and resources are efficiently allocated.
123456789101112# Daily completed work quantities in cubic meters daily_work = [10, 12, 8, 15, 11, 13, 9, 14, 10, 12] # Calculate cumulative progress cumulative_progress = [] total = 0 for qty in daily_work: total += qty cumulative_progress.append(total) print("Daily completed work:", daily_work) print("Cumulative progress:", cumulative_progress)
Cumulative progress shows the total amount of work completed up to each day, providing a clear picture of overall project advancement. By reviewing cumulative progress, you can quickly assess whether the project is on track, ahead, or behind schedule. This information is crucial for identifying potential delays early, allowing you to take corrective action before issues escalate.
12345678910111213141516171819202122import matplotlib.pyplot as plt # Sample data days = list(range(1, 11)) cumulative_progress = [10, 22, 30, 45, 56, 69, 78, 92, 102, 114] milestone_days = [4, 8] milestone_values = [cumulative_progress[3], cumulative_progress[7]] plt.figure(figsize=(8, 4)) plt.plot(days, cumulative_progress, marker='o', label='Cumulative Progress') plt.xlabel("Day") plt.ylabel("Cumulative Work (m³)") plt.title("Construction Progress Over Time") plt.grid(True) # Annotate milestones for day, value in zip(milestone_days, milestone_values): plt.annotate(f"Milestone\nDay {day}", (day, value), textcoords="offset points", xytext=(0,10), ha='center', color='red') plt.legend() plt.tight_layout() plt.show()
1. What is the benefit of plotting cumulative progress in construction projects?
2. How can Python help identify project delays from progress data?
3. Fill in the blank: The ______ function in matplotlib is used to add text annotations to a plot.
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