Moiz (Project Manager) Mashood (Hardware Development) Khizer (Software Development)Moiz (Prototyping and Testing)Under the leadership of Moiz, the team successfully completed the automatic plantNew chat User Write an assignment include two different topics the one is "difference between informed and uninformed search it's types and explain real life examples " and the another topic which is " difference between strong AI and strong method " keeping in view that every topic is explained separately and explainsd well that assignment can contain more than 5000 words ChatGPT Introduction This assignment aims to discuss two different topics. The first topic is about the difference between informed and uninformed search, its types, and real-life examples. The second topic is about the difference between strong AI and strong method. Both of these topics are significant in the field of artificial intelligence and will be discussed in detail below. Topic 1: Difference between Informed and Uninformed Search, Types, and Real-Life Examples In artificial intelligence, a search algorithm is a way of finding a solution to a problem. A search algorithm can be informed or uninformed. Informed search algorithms use heuristic functions to guide the search towards a goal, while uninformed search algorithms do not use any heuristic function. In this section, we will discuss the difference between informed and uninformed search, its types, and real-life examples. 1.1 Difference between Informed and Uninformed Search The main difference between informed and uninformed search is the use of heuristic functions. Heuristics are problem-solving techniques that use a practical approach to find a solution, but not necessarily the best solution. Informed search algorithms use heuristic functions to guide the search towards a goal, while uninformed search algorithms do not use any heuristic function. Informed search algorithms are more efficient than uninformed search algorithms because they use information about the problem to guide the search. For example, in the A* search algorithm, which is an informed search algorithm, the algorithm uses a heuristic function to estimate the cost of the solution from the current node to the goal node. This information helps the algorithm to make informed decisions about which node to expand next. On the other hand, uninformed search algorithms do not use any heuristic function to guide the search. For example, in the depth-first search algorithm, which is an uninformed search algorithm, the algorithm starts at the root node and explores as far as possible along each branch before backtracking. This algorithm is not very efficient because it does not take into account any information about the problem. 1.2 Types of Informed and Uninformed Search Algorithms There are several types of informed and uninformed search algorithms. In this section, we will discuss some of the most commonly used search algorithms. 1.2.1 Uninformed Sear

1. Faculty of Agricultural Engineering &
Technology
Department of Land & Water
Conservation Engineering
Soil Mechanics
LWCE-402
Permeability
& Seepage

2. Quick Sand Condition
Quicksand is a condition and not a soil type. This
condition is created in saturated thick layers of loose
fine sandy soils when disturbed either due to vibration,
such as, from pile driving in the neighborhood, or due to
pressure of flowing water (at the time of heavy pumping
in excavation).
Quicksand forms in saturated loose sand when the sand
is suddenly agitated. When water in the sand cannot
escape, it creates a liquefied soil that loses strength and
cannot support weight. Quicksand can form in standing
water or in upwards flowing water (as from an artesian
spring).

3. Flow Net
The flow of water through a soil can be represented
graphically by a flow net, a form of curvilinear net made
up of a set of flow lines intersected by a set of
equipotential lines.
Flow Line
The paths which water particles follow in the course of
seepage are known as flow lines. Water flows from
points of high to points of low head,
Equipotential lines
As the water moves along the flow line it experiences a
continuous loss of head. If we can obtain the head
causing flow at points along a flow line, then by joining
up points of equal potential we obtain a second set of
lines known as equipotential lines.

4. Flow Net
Hydraulic gradient
The potential drop between two adjacent equipotentials
divided by the distance between them is known as the
hydraulic gradient.

5. Calculation of seepage quantities
Nd = number of potential drops
Nf = number of flow channels
h = total head loss
q = total quantity of unit flow.

6. Flow Net Construction

7. Flow Net Construction

8. Flow Net Construction

9. Flow Net Construction

10. A river bed connsists of a layer of sand 8.25 m thick overlying
impermeable rock; the depth of water is 2.5 m. A long coffer dam
5.50 m wide is formed by driving two lines of sheet piling to a
depth of 6 m below the level of the river bed, and excavation to a
depth of 2 m below bed the level is carried out within the
cofferdam. The water level within the cofferdam is kept at
excavation level by pumping. If the flow of water into the
cofferdam is 0.25 m3/hr per unit length, what is the coefficient of
permeability of the sand? What is the hydraulic gradient
immediately below the excavated surface?

13. Design of Soil Filter
As seen above, water seeping out of the soil can lead to
piping and therefore drainage should be provided in such
situations to ensure ground stability. To prevent soil
particles being washed into the drainage system, soil filters
can be provided as the interface between base material
and drain. The design procedure for a filter is largely
empirical, but it must comprise granular material fine
enough to prevent soil particles being washed through it
and yet coarse enough to allow the passage of water.
 D15 filter > 5 × D15 of base material
 D15 filter < 5 × D85 of base material

14. Design of Soil Filter
The formulae used in the specification of the filter
material are
 D15 filter > 5 × D15 of base material
 D15 filter < 5 × D85 of base material
The first equation ensures that the filter layer has a
permeability several times higher than that of the soil it
is designed to protect. The requirement of the second
equation is to prevent piping within the filter. The ratio
D15 (filter)/D85 (base) is known as the piping ratio.

15. Design of Soil Filter
Determine the approximate limits for a filter material
suitable for the material shown in Fig. 2.13.

16. Design of Soil Filter
Solution:
From the particle size distribution curve:
D15 = 0.01 mm; D85 = 0.2 mm
UsingTerzaghi’s method:
Maximum size of D15 for filter =5×D85 of base =5×0.2=1.0 mm
Minimum size of D15 for filter =5×D15 of base = 5×0.01 = 0.5 mm
This method gives two points on the 15% summation line. Two lines
can be drawn through these points roughly parallel to the grading
curve of the soil, and the space between them is the range of material
suitable as a filter (Fig. 2.13).