How Does the Quadratic Function Model Smooth Transitions in Highway Design?

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In summary, the quadratic function y=ax^2+bx+c is called the transition curve in highway design, providing a smooth transition between peaks and valleys. Given a road with an initial gradient of 3%, represented by y=ax^2+0.3x+c, the equation of the transition curve can be found by plugging in x=200 and y=1105, and x=1000 and y=1105. This results in two equations in two unknowns (a and c), which can be easily solved to find the equation of the transition curve.
  • #1
lionely
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Homework Statement


In highway design, for civil engineers the quadratic function y= ax^2+bx +c is called the transition curve, because its properties provide a smooth transition between peaks and valleys. A road with an initial gradient of 3% can be represented by the formula y=ax^2+0.3x+c , where y is the elevation and x is the distance along the curve. Suppose the elevation of the road is 1105 feet at points 200 feet and 1000 feet along the curve. Find the equation of the transition curve.


Is this a theory of quadractic equation problem? Where I should use the roots and find the new equation?
 
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  • #2
lionely said:

Homework Statement


In highway design, for civil engineers the quadratic function y= ax^2+bx +c is called the transition curve, because its properties provide a smooth transition between peaks and valleys. A road with an initial gradient of 3% can be represented by the formula y=ax^2+0.3x+c , where y is the elevation and x is the distance along the curve. Suppose the elevation of the road is 1105 feet at points 200 feet and 1000 feet along the curve. Find the equation of the transition curve.

Is this a theory of quadratic equation problem? Where I should use the roots and find the new equation?

Simply plug-in x=200 and y=1105 into y=ax^2+0.3x+c to get one equation.

Plug-in x=1000 and y=1105 into y=ax^2+0.3x+c to get another equation.

You now have two equations in two unknowns. solve them for a and c
 
  • #3
Sigh really? =/
 
  • #4
lionely said:
Sigh really? =/

Why "sigh"? You have two simple linear equations for a and c, and solving them is easy.
 
  • #5
I said sigh because I didn't realize it was that easy. :(
 
  • #6
Occam's razor!
 

Related to How Does the Quadratic Function Model Smooth Transitions in Highway Design?

1. What is the Theory of Quadratic Problem?

The Theory of Quadratic Problem is a mathematical theory that deals with finding the maximum or minimum value of a quadratic function. It involves finding the optimal solution to a problem with a quadratic objective function and linear constraints.

2. What are the components of a Quadratic Problem?

The components of a Quadratic Problem are the objective function, which is a quadratic function, and the constraints, which are linear equations or inequalities. The goal is to find the values of the variables that will optimize the objective function while satisfying the constraints.

3. What is the difference between a Quadratic Problem and a Linear Problem?

A Linear Problem involves finding the optimal solution to a problem with a linear objective function and linear constraints, while a Quadratic Problem involves finding the optimal solution to a problem with a quadratic objective function and linear constraints. This means that in a Quadratic Problem, the objective function contains squared terms, while in a Linear Problem, the objective function does not.

4. How is the optimal solution to a Quadratic Problem found?

The optimal solution to a Quadratic Problem is found by using a method called optimization. This involves finding the critical points of the objective function, which are the points where the derivative of the function is equal to zero. The optimal solution is then determined by evaluating the objective function at these critical points and comparing the values.

5. What are the real-world applications of the Theory of Quadratic Problem?

The Theory of Quadratic Problem has many real-world applications, such as in economics, engineering, and finance. It can be used to optimize production processes, minimize costs, maximize profits, and solve other optimization problems in various industries. It is also used in the field of machine learning and data analysis to find the best fitting model for a given dataset.

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