The building is important strut industry of our national economy. But the safety accidents in building domain occur frequently every year.
The safety problem of building directly influences or even restricts the development of building industry. Support Vector Machine (SVM) is a kind of new machine learning method developed on the basis of statistical learning theory.
This method based on the principle of structural risk minimization can solve the problem of overfitting effectively and has good generality capability and better classification accuracy.
In this paper, the author applied this method in safety management of building and carried on the study of early warning system of building safety based on SVM.
At last, we carried on the data experiment to this problem and proved that the method of SVM has good generality capability.

## Introduction

At present, the building industry has already become the important strut industry
with the development of our national economy. The safety problem of building domain
relates to the people life and nation property safety. Its direct aftereffect harms
the people’s vital interest and influences the situation of social stability. Because
the building industry has these characteristics of singleness and complexity and
open-air work and upper-air work and labor-intensiveness, the safety accidents in
building domain occur frequently. According to the incomplete statistics, the annual
number of casualties is about thousands of people and direct economic losses are
over ten billion Yuan. So the safety problem in building domain has aroused the
universal attention of society. In view of this situation, the government has promulgated
a series of laws and regulations to strengthen the safety production of building
domain. Some management personnel and technical personnel of building enterprise
and some experts have also carried through various studies and discussions to safety
production of building, including having made some achievements in building safety
evaluation and early warning. In allusion to the current situation of building safety
management of our country, we carried on the study of early warning of building
safety based on Support Vector Machine (SVM) in this paper. We hope that to develop
this study can reduces casualty and property losses. In addition, it has certain
model significance for the building safety management way of our country.

## Support vector machine

Support Vector Machine is a kind of new machine learning method developed on the
basis of statistical learning theory. This method based on the principle of structural
risk minimization can solve the problem of overfitting effectively and has good
generality capability and better classification accuracy. It is becoming a new research
focus of machine learning field after pattern-recognition and neural network.

SVM is used to find the optimal separating hyperplane of linear classification problem
first. The so-called optimal separating hyperplane not only can be used to separate
the data, but also can maximize the margin. So, the problem of constructing the
optimal separating hyperplane can be turned into the following optimization problem:

_{
}
(1)

s.t. _{
},
i=1,2,…,_{}

The above problem can be transformed to the following dual problem by using Lagrange
optimization method:

_{
}

s.t. _{
}

_{
}≥0,
i=1,2,…,_{}
(2)

where _{
}
are the Lagrange multipliers.

Solving Equation (2) with constraints Equation determines the Lagrange multipliers,
and the optimal separating hyperplane is given by,

_{}
(3)

where sgn ()denotes the sign function.

So far the discussion has been restricted to the case where the training data is
linearly separable. However, in general this will not be the case. In the case where
it is misclassification, alternatively a more complex function can be used to describe
the boundary. To enable the optimal separating hyperplane method to be generalized,
Cortes and Vapnik (1995) introduced non-negative variables,_{}≥0. The _{
}
are a measure of the misclassification errors. The optimization problem is now posed
so as to minimize the classification error as well as minimizing the bound on the
VC dimension of the classifier. The generalized optimal separating hyperplane
is determined by the vector _{
}

_{
}
(4)

where _{
}
is a given viable. The generalized optimal separating hyperplane is nearly the same
as to linearly separable problem, just the constraints Equation turns into:

*0≤*_{}*≤C
, i=1,2…l*.

To non-linear problem, we can transform it to the problem of a high dimensional
feature space by the use of reproducing kernels. The idea of the kernel function
is to enable operations to be performed in the input space rather than the potentially
high dimensional feature space. Hence the inner product does not need to be evaluated
in the feature space. This provides a way of addressing the curse of dimensionality.
So the optimal separating hyperplane is transformed to:

_{
}
(5)

where _{
}

Hence, if we select different kernel function, we can acquire different support
vector machines.

## Early warning of building safety based on SVM

The early warning problem can be regarded as classification problem. Considering
the simplest situation, we can classify it into having warning and no having warning.
Then we regard the early warning problem as classification problem of two classes
by integrating the history data and the present data. Hence, we can solve it by
SVM. If we hope to infer different warning degree, we may regard early warning problem
as classification problem of multiclasses.

The SVM learning algorithms include SVMlight, SMO, Chunking, Decomposing and so
on. We select SVMlight when constructing the early warning system. The systematic
design idea is to design the effective algorithm and choose working set of including
q non-0 weight and decompose the original optimal problem.

The process of early warning of building safety can be regarded as a process of
pattern recognition. Comparing the new early warning sample of unknown warning degree
with standard sample of known warning degree, we can determine mode category of
early warning which new early warning sample should belong to.

According to the above analysis, we constructed the framework of deal with early
warning problem by computer as Figure1:

In Figure 1, the man-machine interface is the program interface which users use
the early warning system. The original data trained and relevant parameters are
inputted through the man-machine interface and the alarm signal is also displayed
through the man-machine interface. The knowledge acquisition subsystem formed by
SVM classification algorithm is a core of the early warning system. The knowledge
base subsystem is a carrier of the early warning knowledge, and is also a guide
part of the warning subsystem. Warning subsystem is equivalent to the wrong inspection
system in the pattern recognition system. Under the drive of the new early warning
data, it received the warning result by the calculation of decision-making function.

In Figure 1, the man-machine interface is the program interface which users use
the early warning system. The original data trained and relevant parameters are
inputted through the man-machine interface and the alarm signal is also displayed
through the man-machine interface. The knowledge acquisition subsystem formed by
SVM classification algorithm is a core of the early warning system. The knowledge
base subsystem is a carrier of the early warning knowledge, and is also a guide
part of the warning subsystem. Warning subsystem is equivalent to the wrong inspection
system in the pattern recognition system. Under the drive of the new early warning
data, it received the warning result by the calculation of decision-making function.

According to table 2, we can know the test results are completely consistent with
original results. This also indicates SVM has good generality capability and better
classification accuracy.

## References

1. Nai-yang Deng, Ying-jie Tian, **The new method of data mining:
support vector machine**, Science press,
China
, 2004

2. Hui Zhang, **Application of support vector machine in data mining**,
Computer Engineering, vol 30(6), p. 7-8, March 2004

3. Cristianini N., Shawe-Taylor J., **Introduction to Support Vector
Machines, **Cambridge, Cambridge University Press, 2000, 52~767

4. http://support.vector.net