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Top 15 C# Interview Question

2023-03-03T03:20:00.004-08:00

C# is a Microsoft-developed, general-purpose, object-oriented programming language.

It is one of the most flexible programming languages with a powerful runtime, access to a huge amount of library and platform functionality, and a strong set of abstractions.

Using C#, you are prone to create web applications or gaming apps. C# has some fantastic features like automatic garbage collection, interfaces, etc. which help build better applications.

C# is such a widely-used programming language, a plethora of big and small organizations base their products using it. So, prepare yourself with basic and advanced level C# questions to ace the interviews.

Top 15 C# Interview Questions and Answers.

Now, let us take a look at the top C# interview questions that you might face.

1.Explain the difference between .NET and C#?

C# is a programming language and .NET is a framework .NET has Common Language Runtime (CLR), which is a virtual component of .NET framework.

C# is a part of .NET and has the following features −

Boolean Conditions
Automatic Garbage Collection
Standard Library
Assembly Versioning
Properties and Events
Delegates and Events Management
Easy-to-use Generics
Indexers
Conditional Compilation
Simple Multithreading
LINQ and Lambda Expressions
Integration with Windows

2.What is IL (Intermediate Language) code?

Intermediate language (IL) is an object-oriented programming language designed to be used by compilers for the .NET Framework before static or dynamic compilation to machine code.

IL(Intermediate language) code is a partially compiled code .JIT(Just in Time compiler compiled intermediate language code to native code (machine code).

3.What is CLR(Common Language Runtime)?

.NET CLR is a runtime environment that manages and executes the code written in any .NET programming language. CLR is the virtual machine component of the .NET framework.

The main components of CLR are :

Common type system
Common language speciation
Garbage Collector
Just in Time Compiler
Metadata and Assemblies

4.What is Managed and Unmanaged code?

Any language that is written in the .NET framework is managed code. Managed code use CLR.

Benefits: Provides various services like a garbage collector, exception handling, etc.

The code developed outside the .NET framework is known as unmanaged code. Applications that do not run under the control of the CLR are said to be unmanaged.

They don’t provide services of the high-level languages and therefore, run without them. Such an example is C++.

5.Explain the importance of Garbage collector?

Garbage collection is the process of freeing up memory that is captured by unwanted objects. When you create a class object, automatically some memory space is allocated to the object in the heap memory. Now, after you perform all the actions on the object, the memory space occupied by the object becomes waste. It is necessary to free up memory. Garbage collection happens in three cases:

If the occupied memory by the objects exceeds the pre-set threshold value.
If the garbage collection method is called
If your system has low physical memory

6.Explain Boxing and Unboxing?

The two functions are used for typecasting the data types:

Boxing: Boxing converts value type (int, char, etc.) to reference type (object) which is an implicit conversion process using object value.

Unboxing: Unboxing converts reference type (object) to value type (int, char, etc.) using an explicit conversion process.

7.Difference Between array and arraylist?

An array is a collection of similar variables clubbed together under one common name. While ArrayList is a collection of objects that can be indexed individually. With ArrayList you can access a number of features like dynamic memory allocation, adding, searching, and sorting items in the ArrayList.

8.What are Generic collections?

In C# collections, defining any kind of object is termed okay which compromises C#’s basic rule of type-safety. Therefore, generics were included to type-safe the code by allowing re-use of the data processing algorithms. Generics in C# mean not linked to any specific data type. Generics reduce the load of using boxing, unboxing, and typecasting objects. Generics are always defined inside angular brackets <>.

9.How do we handle exception in C#?

Exceptions provide a way to transfer control from one part of a program to another. C# exception handling is built upon four keywords: try, catch, finally, and throw.
try − A try block identifies a block of code for which particular exceptions is activated. It is followed by one or more catch blocks.
catch − A program catches an exception with an exception handler at the place in a program where you want to handle the problem. The catch keyword indicates the catching of an exception.
finally − The finally block is used to execute a given set of statements, whether an exception is thrown or not thrown. For example, if you open a file, it must be closed whether an exception is raised or not.
throw − A program throws an exception when a problem shows up. This is done using a throw keyword.

10.What are extension methods in C#?

Extension methods help to add new methods to the existing ones. The methods that are added are static. At times, when you want to add methods to an existing class but don’t perceive the right to modify that class or don’t hold the rights, you can create a new static class containing the new methods. Once the extended methods are declared, bind this class with the existing one and see the methods will be added to the existing one.

11.What are the differences between ref and out keywords?

C# ref keywords pass arguments by reference and not value. To use the ‘ref’ keyword, you need to explicitly mention ‘ref’.
C# out keywords pass arguments within methods and functions.
‘out’ keyword is used to pass arguments in a method as a reference to return multiple values. Although it is the same as the ref keyword, the ref keyword needs to be initialized before it is passed. Here, The out and ref keywords are useful when we want to return a value in the same variables that are passed as an argument.

12.What are Properties in C#?

Properties in C# are public members of a class where they provide the ability to access private members of a class. The basic principle of encapsulation lets you hide some sensitive properties from the users by making the variables private. The private members are not accessible otherwise in a class. Therefore, by using properties in C# you can easily access the private members and set their values.

The values can be easily assigned using get and set methods, also known as accessors. While the get method extracts the value, the set method assigns the value to the variables.

13.What are events?

Events are user actions such as key press, clicks, mouse movements, etc., or some occurrence such as system generated notifications. Applications need to respond to events when they occur. For example, interrupts. Events are used for inter-process communication.
The events are declared and raised in a class and associated with the event handlers using delegates within the same class or some other class. The class containing the event is used to publish the event. This is called the publisher class. Some other class that accepts this event is called the subscriber class. Events use the publisher-subscriber model.
A publisher is an object that contains the definition of the event and the delegate. The event-delegate association is also defined in this object. A publisher class object invokes the event and it is notified to other objects.
A subscriber is an object that accepts the event and provides an event handler. The delegate in the publisher class invokes the method (event handler) of the subscriber class.

14.What are difference between abstract class and interface ?

Abstract classes are classes that cannot be instantiated i.e. that cannot create an object. The interface is like an abstract class because all the methods inside the interface are abstract methods.
Surprisingly, abstract classes can have both abstract and non-abstract methods but all the methods of an interface are abstract methods.
Since abstract classes can have both abstract and non-abstract methods, we need to use the Abstract keyword to declare abstract methods. But in the interface, there is no such need.

15. What are Indexers in C#?

Indexers are called smart arrays that allow access to a member variable. Indexers allow member variables using the features of an array. They are created using the Indexer keyword. Indexers are not static members.
For ex. Here the indexer is defined the same way.

Mechanical and durability Behavior of fiber reinforced concrete incorporating deferent types of natural, pp and steel fibers

2021-08-25T05:01:00.008-07:00

1. Introduction

As an important building material, concrete has been widely used in civil engineering applications such as bridges and roads engineering, and the related experimental study of the mechanical properties of concrete was also fruitful . With the vigorous development of engineering construction, high-performance concretes such as fiber-reinforced concrete was applied gradually in important engineering structures . Among these high-performance concretes, for the advantages of low cost, easy fabrication, and performance improvements, obviously, steel fiber-reinforced concrete was used widely in the current engineering field . However, the study showed that uneven incorporation of steel fiber would affect the fluidity and uniformity of concrete mixing and even result in fiber bonding, which eventually affects the reinforcement effect of mechanical properties. Up to now, most research paid attention on the improvement effect of different types of fiber or optimum fiber content, but little literature paid attention on the difference of improvement effect by various stirring technologies. As a kind of new stirring technology, compared to traditional stirring technology, vibratory mixing technology could effectively improve the distribution of fibers in concrete, further increase the density of steel fiber concrete, and finally improve the mechanical properties of steel fiber-reinforced concrete . But, at present, vibratory mixing technology has not been widely used in engineering, and research on its improvement of concrete mechanical properties is inadequate at home and abroad. For such reasons, in this paper, different steel fiber incorporation volume concrete test specimens of various mix proportions were prepared, which were made by different stirring technologies. And then, compression test, flexural test, splitting tensile test, and bending test were conducted; finally, the differences of workability and mechanical properties of steel fiber concrete made by vibratory mixing and the traditional mixing technologies, respectively, were compared and analyzed.

2. Materials and Test Programs

2.1. Materials

2.1.1. Steel Fiber

The physicochemical parameter of steel fiber should meet the requirements of JGT 472-2015. The length of steel fiber should be 20 mm∼60 mm and diameter or equivalent diameter should be 0.3 mm∼1.2 mm; length to diameter ratio was 30∼65.

2.1.2. Cement

P.O 42.5 ordinary Portland cement was used in this paper, and each performance index of cement and its strength of 3 days and 28 days were checked according to the performance index of “General Portland Cement” (GB175-2007).

2.1.3. Fine Aggregate

Good quality graded sand was selected, and fineness modulus should be controlled in 2.3 to 3.0; fine aggregate performance was checked according to GB14684-2011.

2.1.4. Coarse Aggregate

Test selection of hard texture, graded continuous gravel, and aggregate shape with a more uniform edge polyhedron was made as well, with a particle size of 5 mm∼20 mm and clay content <1%. The “standard test method for building pebbles, gravel” (GB/T14685-2011) was used as test performance indicators of the coarse aggregate.

2.1.5. Admixture

Polycarboxylate superplasticizer was used as admixture, with water reduction rate not less than 25%. The amount of admixture was 0.5%∼1% of cement content.

2.1.6. Mineral Admixture

Addition of fly ash should be consistent with the provisions of GB/T1596.

2.2. Mixture Proportion Design

The purpose of this experiment is to study the improvement of mechanical properties of different types of steel fiber-reinforced concrete (SFRC) which was made by ordinary stirring and vibratory mixing, respectively. In the field of engineering, steel fiber-reinforced concrete was always used as high-strength concrete, so in this paper, two kinds of high-strength concrete C50 and C60 were studied in this paper, and the amounts of steel fiber admixture were 0.5%, 1%, 1.5%, and 2%, respectively. The specific mix i

2.3. Sample Preparation

To ensure uniform distribution of basalt fibers in the mix, sand and macadam were mixed firstly, and then, cement and fiber were added. After the mixtures were mixed for 30 seconds, the water and additives were added during the course of stirring. The stirring time of steel fiber-reinforced concrete was 3 minutes; and the mixing process is shown in Figure 1

The prepared mixture was put into the test mold to vibrate, and then, it was made flat. The mold was removed after 48 hours maintenance, and then specimens were cured in the standard curing room at the temperature of 20°C and relative humidity of 97%. The curing process is shown

3. Experimental Programs

3.1. Cube Compression of Steel Fiber-Reinforced Concrete

The standard length of 150 mm cube specimen was used in the cube compressive strength test, and the methods and procedures of “ordinary concrete mechanical properties test method standard” GB/T 50080-2016 and “test method for fiber concrete” (CECS 13-2009) were referred to conduct the test. The constant-speed stress control was used in this test, and loading speed was 0.6 MPa/s; the specimen would be automatically unloaded, and the strength of damage was recorded by the machine. The cube compressive strength testing machine is shown in Figure 5.

4.1. Cube Compressive Strength of Steel Fiber Concrete

4.1.1. Cube Compressive Strength of Steel Fiber Concrete Made by Traditional Stirring

It can be seen from Figure 8(a) that, for the traditional mixing method, the compressive strength of steel fiber-reinforced concrete CF50 increases as the steel fiber content increases. At the fiber content of 0.5%, 1%, 1.5%, and 2%, the concrete compressive strength increases by 7.05%, 13.79%, 18.17%, and 20.85%. Figure 8(a) shows that while fiber content is less than 1%, the compressive strength increasing rate is faster; as the fiber content is more than 1% (e.g., 1.5% and 2%), the compressive strength increasing rate becomes slow.

(a)

(b)

Figure 8(b) details that, similar to CF50, the compressive strength of steel fiber-reinforced concrete CF60 also improves with the increase of steel fiber content. At the fiber content of 0.5%, 1%, 1.5%, and 2%, the concrete compressive strength increases by 6.33%, 20.59%, 24.57%, and 26.35%. Figure 8(b) also shows that while fiber content is less than 1%, the compressive strength increasing rate is faster; as the fiber content is more than 1% (e.g., 1.5% and 2%), the compressive strength increasing rate becomes slow.

Comparing CF50 to CF60, it can be found that fiber-reinforced effect of compressive strength of high-strength concrete CF60 is higher than that of CF50; for example, at the same fiber content of 1%, the compressive strength increases by 20.59% in CF60; in CF50, the value is 13.79%. In other words, for vibratory mixing, steel fiber enhanced effect of high-strength concrete is more obvious.

4.1.2. Cube Compressive Strength of Steel Fiber Concrete Made by Vibratory Mixing

Figure 9(a) shows that, for the vibratory mixing mode, with the increase of the steel fiber content, the cubic compressive strength of concrete CF50 increases continuously. At the fiber content of 0.5%, 0.75%, 1%, 1.5%, and 2%, the concrete compressive strength increases by 10.23%, 11.35%, 12.12%, 13.79%, and 17.71%. Figure 9(a) shows that while fiber content is less than 0.5%, the compressive strength increasing rate is faster; as the fiber content is more than 0.5% (e.g., 1.5% and 2%), the compressive strength increasing rate becomes slow.

(a)

(b)

Figure 9(b) shows that, similar to CF50, the compressive strength of CF60 also improves with the increase of steel fiber content. At the fiber content of 0.5%, 1%, 1.5%, and 2%, the concrete compressive strength increases by 7.9%, 14.14%, 19.96%, and 22.89%. Figure 9(b) also shows that while fiber content is less than 1%, the compressive strength increasing rate is faster; as the fiber content is more than 1% (e.g., 1.5% and 2%), the compressive strength increasing rate becomes slow.

Comparing CF50 to CF60, it can be found that fiber-reinforced effect of compressive strength of high-strength concrete CF60 is higher than that of CF50; for example, at the same fiber content of 1.5%, the compressive strength increases by 19.96% in CF60; in CF50, the value is 13.79%. In other words, for vibratory mixing, steel fiber enhanced effect of high-strength concrete is more obvious.

4.1.3. Effect of Different Mixing Methods on the Compressive Properties of Concrete

It can be seen from Figure 10(a) that, with the increase of the steel fiber content, the cubic compressive strength of CF50 concrete increases continuously. Comparing to traditional mixing concrete, with the same steel fiber content, the concrete made by vibratory mixing is equipped with higher compressive strength. At the fiber content of 0%, 0.5%, 0.75%, 1%, 1.5%, and 2%, comparing to traditional mixing concrete, the compressive strength of vibratory mixing concrete increases by 8.18%, 11.40%, 8.80%, 6.59%, 4.17%, and 5.36%. Figure 10(a) also shows that while fiber content is less than 0.5%, the compressive strength improvement is faster; for example, at 0.5% fiber content, the compressive strength of vibratory mixing concrete increased by 11.4%; as the fiber content is more than 0.5% (e.g., 1.5% and 2%), the compressive strength increasing rate becomes slow.

(a)

(b)

Figure 10(b) shows that, with the increase of the steel fiber content, the cubic compressive strength of CF60 concrete increases continuously. Comparing to traditional mixing concrete, at the same steel fiber content, the concrete made by vibratory mixing is equipped with higher compressive strength. At the fiber content of 0%, 0.5%, 1%, 1.5%, and 2.0%, comparing to traditional mixing concrete, the compressive strength of vibratory mixing concrete increases by 11.58%, 13.22%, 5.61%, 7.54%, and 8.53%. Figure 10(b) also shows that while fiber content is less than 0.5%, the compressive strength improvement is faster, for example, at 0.5% fiber content, the compressive strength of vibratory mixing concrete increased by 13.22%; as the fiber content is more than 0.5% (e.g., 1.5% and 2%), the compressive strength increasing rate becomes slow.

Comparing CF50 to CF60, it can be found that fiber-reinforced effect of compressive strength of high-strength concrete CF60 is higher than that of CF50; for example, at the same fiber content of 0.5%, the compressive strength increases by 13.22% and 11.40% in CF60 and CF50, respectively.

4.2. Splitting Tensile Strength of Steel Fiber Concrete

4.2.1. Splitting Tensile Strength of Steel Fiber Concrete Made by Tradition Mixing

It can be seen from Figure 11(a) that, for the traditional mixing method, the splitting tensile strength of steel fiber-reinforced concrete CF50 increases as the steel fiber content increases. At the fiber content of 0.5%, 0.75%, 1%, 1.5%, and 2%, the concrete splitting tensile strength increases by 1.82%, 6.22%, 7.79%, 25.26%, and 35.41%. Figure 11(a) shows that while the fiber content is less than 1%, the splitting tensile strength increases slowly, while the fiber content is more than 1% (e.g., 1.5% and 2.0%), the splitting tensile strength increases rapidly.

(a)

(b)

Figure 11(b) details that, similar to CF50, the splitting tensile strength of steel fiber-reinforced concrete CF60 also improves with the increase in steel fiber content. At the fiber content of 0.5%, 1%, 1.5%, and 2%, the concrete splitting tensile strength increases by 6.39%, 18.18%, 30.71%, and 36.86%. Figure 11(b) also shows that while the fiber content is less than 1%, the splitting tensile strength increases slowly, while the fiber content is more than 1% (e.g., 1.5% and 2.0%), the splitting tensile strength increases rapidly.

Comparing CF50 to CF60, it can be found that, at the same fiber content, fiber reinforcement effect of splitting tensile strength of high-strength concrete CF60 is higher than that of CF50; for example, at the fiber content of 1%, the splitting tensile strength increases by 18.18% and 7.79% in CF60 and CF50, respectively. In other words, for traditional stirring, steel fiber enhanced effect of high-strength concrete is more obvious.

4.2.2. Effect of Steel Fiber Content on Splitting Tensile Strength of Concrete under Vibratory Mixing

It can be seen from Figure 12(a) that, for the traditional mixing method, the splitting tensile strength of steel fiber-reinforced concrete CF50 improves as the steel fiber content increases. At the fiber content of 0.5%, 0.75%, 1.0%, 1.5%, and 2.0%, the concrete splitting tensile strength increases by 4.44%, 7.96%, 11.99%, 28.57%, and 36.99%. Figure 12(b) also shows that while the fiber content is less than 1%, the compressive strength increases slowly, while the fiber content is more than 1% (e.g., 1.5% and 2.0%), the splitting tensile strength increases rapidly.

(a)

(b)

Figure 12(b) shows that, similar to CF50, the splitting tensile strength of steel fiber-reinforced concrete CF60 also improves with the increase obviously in steel fiber content. At the fiber content of 0.5%, 1%, 1.5%, and 2%, the concrete splitting tensile strength increases by 12.11%, 28.74%, 43.23%, and 66.75%.

Comparing CF50 to CF60, it can be found that, at the same fiber content, fiber reinforcement effect of splitting tensile strength of high-strength concrete CF60 is higher than that of CF50; for example, at the fiber content of 1%, the splitting tensile strength increases by 28.74% and 11.99% in CF60 and CF50, respectively. In other words, for vibratory mixing, steel fiber enhanced effect of high-strength concrete is more obvious.

4.2.3. Effect of Different Mixing Methods on the Splitting Tensile Properties of Steel Fiber Concrete

It can be seen from Figure 13(a) that, with the increase of the steel fiber content, the splitting tensile strength of CF50 concrete improves continuously. Comparing to traditional mixing concrete, at the same steel fiber content, the concrete made by vibratory mixing is equipped with higher compressive strength. At the fiber content of 0%, 0.5%, 0.75%, 1%, 1.5%, and 2%, comparing to traditional mixing concrete, the splitting tensile of vibratory mixing concrete increases by 1.82%, 4.44%, 3.48%, 5.78%, 4.51%, and 3.01%. Figure 13(a) also shows that, at the fiber content of 1.0%, comparing to traditional mixing, the splitting tensile strength of concrete made by vibratory mixing improves obviously.

(a)

(b)

Figure 13(b) shows that, similar to CF50, with the increase of the steel fiber content, the splitting tensile strength of CF60 concrete improves continuously. Comparing to traditional mixing concrete, at the same steel fiber content, the concrete made by vibratory mixing is equipped with higher splitting tensile strength. At the fiber content of 0%, 0.5%, 1%, 1.5%, and 2.0%, comparing to traditional mixing concrete, the splitting tensile strength of vibratory mixing concrete increases by 3.44%, 9.01%, 12.68%, 13.35%, and 26.03%. Figure 13(b) also shows that, at the fiber content of 2.0%, comparing to traditional mixing, the splitting tensile strength of concrete made by vibratory mixing improves obviously.

Comparing CF50 to CF60, it can be found that fiber-reinforced effect of splitting tensile strength of high-strength concrete CF60 is higher than that of CF50; for example, at the same fiber content of 1.0%, the compressive strength increases by 12.68% and 5.78% in CF60 and CF50, respectively.

4.3. Flexural Properties of Steel Fiber Concrete

4.3.1. Flexural Properties of Steel Fiber Concrete Made by Tradition Mixing Conditions

It can be seen from Figure 14(a) that, for the traditional mixing method, the flexural strength of steel fiber-reinforced concrete CF50 increases obviously as the steel fiber content increases. At the fiber content of 0.5%, 0.75%, 1%, 1.5%, and 2%, the concrete flexural strength increases by 11.03%, 15.14%, 29.22%, 58.50%, and 80.75%. Figure 14(a) also shows that while the fiber content is less than 1%, the flexural strength increases slowly, while the fiber content is more than 1% (e.g., 1.5% and 2.0%), the flexural tensile strength increases rapidly.

(a)

(b)

Figure 14(b) shows that, similar to CF50, the flexural strength of steel fiber-reinforced concrete CF60 also improves with the increase of steel fiber content. At the fiber content of 0.5%, 1%, 1.5%, and 2%, the concrete flexural strength increases by 17.91%, 27.02%, 68.24%, and 101.86%. Figure 14(b) also shows that while the fiber content is less than 1%, the flexural strength increases slowly, while the fiber content is more than 1% (e.g., 1.5% and 2.0%), the splitting tensile strength increases rapidly.

Comparing CF50 to CF60, it can be found that, at the same fiber content, fiber reinforcement effect of flexural strength of high-strength concrete CF60 is higher than that of CF50; for example, at the fiber content of 1.5%, the flexural tensile strength increases by 68.24% and 58.50% in CF60 and CF50, respectively. In other words, for traditional stirring, steel fiber enhanced effect of high-strength concrete is more obvious.

4.3.2. Effect of Steel Fiber Content on Flexural Properties of Concrete under Vibratory Mixing

It can be seen from Figure 15(a) that, for the vibratory mixing method, the flexural strength of steel fiber-reinforced concrete CF50 increases obviously as the steel fiber content increases. At the fiber content of 0.5%, 0.75%, 1%, 1.5%, and 2%, the concrete flexural strength increases by 8.06%, 13.82%, 24.51%, 55.76%, and 72.86%. Figure 15(a) also shows that while the fiber content is less than 1%, the flexural strength increases slowly, while the fiber content is more than 1% (e.g., 1.5% and 2.0%), the flexural tensile strength increases rapidly.

Figure 15(b) shows that, similar to CF50, the flexural strength of steel fiber-reinforced concrete CF60 also improves with the increase of steel fiber content. At the fiber content of 0.5%, 1%, 1.5%, and 2%, the concrete flexural strength increases by 8.45%, 19.75%, 47.82%, and 68.94%. Figure 15(b) also shows that while the fiber content is less than 1%, the flexural strength increases slowly, while the fiber content is more than 1% (e.g., 1.5% and 2.0%), the splitting tensile strength increases rapidly.

(a)

(b)

4.3.3. Effect of Different Mixing Methods on the Flexural Performance of Concrete

It can be seen from Figure 16(a) that, with the increase of the steel fiber content, the flexural strength of CF50 concrete improves continuously. Comparing to traditional mixing concrete, at the same steel fiber content, the concrete made by vibratory mixing is equipped with higher flexural strength. At the fiber content of 0%, 0.5%, 0.75%, 1%, 1.5%, and 2%, comparing to traditional mixing concrete, the flexural strength of vibratory mixing concrete increases by 13.55%, 10.61%, 12.34%, 9.50%, 11.67%, and 8.69%. Figure 16(a) also shows that, at the fiber content of 0.0%, comparing to traditional mixing, the splitting tensile strength of concrete made by vibratory mixing improves obviously.

(a)

(b)

Figure 14(b) shows that, similar to CF50, with the increase of the steel fiber content, the flexural strength of CF60 concrete improves continuously. Comparing to traditional mixing concrete, at the same steel fiber content, the concrete made by vibratory mixing is equipped with higher flexural strength. At the fiber content of 0%, 0.5%, 1%, 1.5%, and 2.0%, comparing to traditional mixing concrete, the splitting tensile strength of vibratory mixing concrete increases by 10.01%, 14.04%, 16.73%, 8.94%, and 3.77%. Figure 14(b) also shows that, at the fiber content of 1.0%, comparing to traditional mixing, the flexural strength of concrete made by vibratory mixing improves obviously.

Comparing CF50 to CF60, it can be found that fiber-reinforced effect of flexural strength of high-strength concrete CF60 is higher than that of CF50; for example, at the same fiber content of 1.0%, the flexural strength increases by 16.73% and 9.50% in CF60 and CF50, respectively.

5. Conclusion

In this paper, the compressive strength, splitting tensile strength, and flexural strength of steel fiber-reinforced concrete made by different mix methods were analyzed. The main conclusions are as follows:(1)With the increase in steel fiber content, all of these mechanical properties such as compression strength, flexural strength, and splitting tensile strength improve gradually; especially for flexural strength and splitting tensile strength, the steel fiber reinforcement effect is obvious. At the same fiber content, reinforcement effect of mechanical properties of high-strength concrete is better.(2)The vibratory mixing method can make the steel fiber distribute uniformly in the concrete; as a result, comparing to traditional mixing, the vibratory mixing method can improve the compressive strength, splitting tensile strength, and flexural strength effectively. For example, compressive strength can be improved by 10%, splitting tensile strength can be improved by 15%, and flexural strength can be improved 12%.

Learning to recognize handwritten digits

2021-07-06T18:07:00.004-07:00

The Digits data set of the Scikit-learn library provides numerous data-sets that are useful for testing many problems of data analysis and prediction of the results. Some Scientist claims that it predicts the digit accurately 95% of the times. Perform data Analysis to accept or reject this Hypothesis.

In this project, we are using the Handwritten Digits dataset which is already ready in the sklearn library. we can import the dataset

               from sklearn import datasets

               digits = datasets.load_digits()
Info about Dataset:

                print(digits.DESCR)

OUTPUT:




                main_data = digits['data']
                targets = digits['target']
                len(main_data)






%matplotlib inline

plt.subplot(321)
plt.imshow(digits.images[1791], cmap=plt.cm.gray_r,
interpolation='nearest')

plt.subplot(322)
plt.imshow(digits.images[1792], cmap=plt.cm.gray_r,
interpolation='nearest')

plt.subplot(323)
plt.imshow(digits.images[1793], cmap=plt.cm.gray_r,
interpolation='nearest')

plt.subplot(324)
plt.imshow(digits.images[1794], cmap=plt.cm.gray_r,
interpolation='nearest')

plt.subplot(325)
plt.imshow(digits.images[1795], cmap=plt.cm.gray_r,
interpolation='nearest')

plt.subplot(326)
plt.imshow(digits.images[1796], cmap=plt.cm.gray_r,
interpolation='nearest')

OUTPUT:




Support Vector Classifier:


                         from sklearn import svm
                         svc = svm.SVC(gamma=0.001 , C = 100.)
                         svc.fit(main_data[:1790] , targets[:1790])
                         predictions = svc.predict(main_data[1791:])
                         predictions , targets[1791:]
OUTPUT:

          (array([4, 9, 0, 8, 9, 8]), array([4, 9, 0, 8, 9, 8]))
From SVC we get 100% accuracy
Training Data : 1790
Test Data : 6



Decision Tree Classifier:


                    from sklearn.tree import DecisionTreeClassifier
                    dt = DecisionTreeClassifier(criterion = 'gini')
                    dt.fit(main_data[:1600] , targets[:1600])

                    predictions2 = dt.predict(main_data[1601:])
                    from sklearn.metrics import accuracy_score
                    confusion_matrix(targets[1601:] , predictions2
OUTPUT:


       array([[17,  0,  0,  0,  0,  0,  0,  0,  0,  0],
       [ 0, 17,  0,  0,  1,  0,  0,  0,  2,  0],
       [ 0,  0, 13,  1,  0,  1,  0,  1,  1,  0],
       [ 0,  2,  2,  9,  0,  3,  2,  4,  0,  0],
       [ 0,  0,  0,  0, 18,  0,  1,  2,  0,  1],
       [ 0,  0,  0,  1,  2, 15,  0,  0,  1,  0],
       [ 0,  0,  0,  1,  2,  0, 19,  0,  0,  0],
       [ 0,  0,  0,  2,  1,  0,  0, 17,  0,  0],
       [ 0,  2,  1,  0,  0,  0,  0,  1, 13,  0],
       [ 0,  1,  0,  0,  0,  0,  0,  2,  1, 16]], dtype=int64)

                    accuracy_score(targets[1601:] , predictions2)
OUTPUT:
           0.7857142857142857
From Decision Tree Classifier we get 78 % Accuracy
Training Data : 1600
Test_data : 197


Random Forest Classifier:


                      

               from sklearn.ensemble import RandomForestClassifier
               

               rc = RandomForestClassifier(n_estimators = 150)
               rc.fit(main_data[:1500] , targets[:1500])
               predictions3 = rc.predict(main_data[1501:])
               accuracy_score(targets[1501:] , predictions3)
OUTPUT:

          0.9222972972972973
From Random Forest Classifier we get high accuracy for n_estimators = 150
Training data : 1500
Test Data : 297



Conclusion:
Data maters the most we need a good amount of data for modal.if we have a less data then we can use some other machine learning classifier algorithms like random forest which is also give 92 % accuracy on 1500 trainset which is less data compare to Support vector classifier.


As per our hypothesis, we can say with hyperparameter tunning with different machine learning models or using more data we can achieve near 95% accuracy on the handwritten dataset. But make sure we also have a good amount of test data otherwise the model will get overfit.