The Support Vector Machine ( Svm )

Introduction : To determine the strength, maximum load can be applied and workability of concrete mixture.

Cross-validation method was used to evaluate the predictive power of models. Dataset was equally divided into two parts. Models were fitted on training set and tested on testing set.

Concrete is an artificial material in which the aggregates are bonded together by the cement when mixed with water. With the advancement of technology and increased field of application of concrete and mortars, the strength, workability, durability and other characteristics of the ordinary concrete can be made suitable for any situation. For this, definite proportions of cement, water, fine aggregate, coarse aggregate, mineral admixtures and chemical admixtures are

Many researches had been done on the lightweight concrete to describe the nonlinear behavior. The previous researches faced obstacles concerning the inaccuracy of the models results because of the lack of the experimental results. Therefore, it is necessary to focus on the laboratory program to get many mechanical properties in order to help calibrating a

The machine learning techniques that were utilized in this data analysis project were both applied to the Iris Data Set available at the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Iris). This data was entered into a text file and saved with a .csv extension, then this .csv file was fed into the WEKA software for analysis. One of the techniques utilized was a simple visualization of the data spread, which has some predictive power in clearly demonstrating the spread of the data and thus the likelihood of obtaining the basic measurements of sepal length and width and petal length and width. The algorithm for this is simple, similar to what Excel or any basic spreadsheet can accomplish, yet is still demonstrative of machine learning in one of its simplest forms as earliest conceived (Andrieu et al, 2003). The second round of analysis conducted was a cross-validation analysis, which has more predictive power and is indicative of the stronger analytic powers available in machine learning as it forms a basic prediction of how accurate other predictions will be that is, the algorithm that is used to analyze the data also analyzes and adjusts itself in order to determine how close a fit it can come to the data (Bishop, 2006). The results of these analyses show a fairly normal distribution for sepal length and width but irregular distributions for petal length and width that would require a slightly different approach to certain modes of

Concrete is one of the essential component of public infrastructure and most buildings. It can only be more effective if we used reinforcement with it, because tensile strength without reinforcement is very low. It usually get cracked with time as it is brittle in property. There are some more causes of these cracks are shrinkage, inadequate water for hydration… etc. these cracks causes reinforcement exposes to atmosphere now reinforcement come into contact of O2 & H2O. That can cause corrosion which results in weaken of structure. That being said concrete is a high maintenance material. Concrete cracks and suffer wear and tear. Concrete is not as much flexible that it can handle significant amount of strain.

This work aims to study the seismic performance with different soil condition like hard, medium and soft of multi-storied buildings having plan regularities. The buildings were analyzed by software STAAD Pro. The result obtained in term of percentage change in base shear and peak story shear.

In simple regression, one can build a predictive model to relate the predictor variable, X to a response variable Y through a relationship Y = aX + b.

This project aims to create a Structural Health Monitoring System that will detect and locate damage in a structure using accelerometer sensors. The usual method to create a Structural Health Monitoring System is to use machine learning techniques, by analysing changes in the dynamics of structures in both healthy and damaged states. The issue with this technique is that vast amounts of data needs to be collected through destructive testing which can be impossible for a system that is only a one off (e.g. bridges, skyscrapers, etc.) or large individual product cost. The purpose for this project is to obtain the necessary data using a computer model of a structure, simulating both healthy and damaged states, to be used for machine learning techniques, removing the need for destructive testing.

Yangon, the biggest city in Myanmar, is located near Sagaing fault and many earthquakes with various intensities occurred frequently. Over the past three decades, urbanization in Yangon has been rapidly increasing and most of the buildings in congested downtown of Yangon is reinforced concrete buildings. These buildings are regular in plan and designed for gravity loads only. They are built with minimal consideration of building codes, sound construction and urban planning practices. These buildings have limited lateral resistance and are susceptible to story mechanisms during earthquake loading. Damage estimation is a vital part of the seismic performance evaluation of buildings and other structures with

Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve. Different analyses will deliver different outcomes and thus provide different insights [1].

In user interface model data is retrieved from the dataset and represented in a readable format. On this data pattern recognition and analysis can be performed.

From Figure 2, it is clear that the values predicted by bagging, boost and neural network methods came very close to actual values. On the other hand, the SVM with radial basis function (RBF) kernel and SVM with 3 order of polynomial model generated a horizontal straight line, i.e. did not change at all. Similar trends (shown in Figure 4) were observed for the rest of the parameter sets. This shows that SVM has a poor performance in general as compared to other methods.

With the continuous improvement of modern architectural functions, higher requirements of concrete have been put forward. Under the circumstance of high technology, concrete technology is developed towards high-strength, high-workability and high durability. Since the 1990s, along with a variety of new cementitious materials, mineral admixtures, superplasticizer and the new development and application of other additives, the production of concrete with good workability, excellent mechanical properties and also high durability can become a reality (Laskar, 2011). This new type of concrete is called high-performance concrete (HPC). Compared to the conventional concrete, HPC has high strength, high resistance and high durability. Therefore, high-performance concrete is the inevitable result of the development of modern concrete technology.

The research seeks to compare the difference in measurement of flexural strength of palm kernel shell (PKS) concrete, using direct and indirect methods (beam and splitting cylinder specimen).The Palm kernel shell (PKS) was subjected to various physical tests and values obtained are as follows; specific gravity of Palm kernel shell (PKS) 1.3, Aggregate impact value 10.23.The Concrete was cast using two mix ratios 1:1.5:3 and 1:2:4. The concrete cubes, cylinders, and beams were crushed at 7, 14 and 28days in other to determine the compressive strength and flexural strength of the concrete for the various mixed. The 28th day compressive strength was 12.30N/mm2 and 20N/mm2, for the two mixed ratio respectively. The 28th day flexural strength was 2.03N/mm2 and 1.10N/mm2 for the beam and cylinder specimen respectively. The student t-test showed that there is no significant difference between the tensile strength measured using direct and indirect