As an important branch of artificial intelligence, neural network is an effective tool to deal with nonlinear problems. In terms of characteristics, neural network has good nonlinear mapping ability, good adaptability and fault tolerance. When using neural network to calculate problems, learning rules can be obtained directly from data without prior model. Therefore, neural network can be used to solve some problems that are difficult to be solved by traditional mathematical methods, and can also be used to deal with complex problems that are difficult to model. The so-called economic management of construction is actually the effective prediction and control of construction activities. In this process, it is necessary to complete the real description and analysis of architectural activities and use the law to complete the reasonable explanation of various phenomena.
However, in practical work, building economic management will involve a large number of variables, and most of them are fuzzy. In this case, there is often a nonlinear relationship between variables and constants, and then it is difficult to reasonably explain variables with traditional mathematical analytical formulas. At present, in building economic management, neural network can solve complex problems in management. Neural network has a good application prospect in engineering cost prediction, economic early warning, bidding and many other aspects.
2 neural network in the application of building economic management
2. Application of1in cost prediction
In the aspect of construction cost prediction, neural network can be applied to the estimation of construction cost. Using BP network, we can build a project cost prediction model and truly complete the simulation of project production, management and other activities. By analyzing the components of the cost and tracking the composition of the project value chain, we can adapt to the cost changes of the project and then complete the project cost forecast. At present, the application of neural network in cost prediction has been verified by engineering examples, and its application effect is obviously better than traditional methods. When neural network is applied to engineering evaluation, the "feature extractor" of the network can be used to extract engineering features.
Neural network can find out the regular relationship between budget data and engineering characteristics from a large number of engineering data, and complete the correction of data deviation caused by other factors, thus ensuring the effectiveness of prediction results. In addition, because the neural network adopts parallel data processing, it can complete the project cost prediction as soon as possible, and then meet the cost analysis requirements of construction operations. Using neural network to predict the project cost can help the construction contractor manage the project funds better, and then avoid the shortage of funds.
2.2 Application in Risk Early Warning
In the construction management activities, there will be many risks such as financial risk, financial risk and market risk, which makes the construction economic management risky. Using neural network can complete the risk early warning, thus reducing the risk of building economic management. Neural network system can be regarded as an investment decision-making tool when using neural network to evaluate the risks and benefits of engineering operation. Specifically, it is necessary to use the nonlinear mapping and pattern analysis ability of neural network in order to establish a dynamic risk early warning system.
On this basis, we need to take the risk source factor as the input unit of the system, and then get the corresponding risk level and possible risk interval. There are many risk source factors, such as project complexity and unforeseen factors. At present, a risk early warning system needs to be composed of multiple neural networks. For example, the investment risk early warning system of construction projects consists of several ART networks, BP networks and a MAXNET network.
2.3 Application in project bidding
In the fierce market competition environment, construction enterprises need to analyze the factors affecting the bidding decision of engineering projects in advance in order to win in the competition. The factors involved include market situation, competitors, engineering situation and many other fields, and the factors themselves are mostly fuzzy variables, so it is difficult to determine the influence of factors on bidding quotation. Neural network can analyze the relationship between factors and bidding quotation according to similar engineering information in the past, and then complete the reasoning of engineering quotation.
According to this reasoning, the contractor can determine the bidding strategy that needs to be adopted. At the same time, combined with the project cost forecast results, the contractor can complete the determination of the bid price, and then gain greater competitive advantage. At present, neural network has been applied to project bidding management to some extent, and related decision support system and bidding expert system have been put forward. Input the management rate, competitors' situation, market situation and other factors into the input layer of the system, and the quotation rate of the project bidding quotation can be obtained.
2.4 Other applications
In addition to the above aspects, neural network can also be applied to many other aspects of building economic management. First of all, when managers of construction enterprises make business decisions, neural networks can provide decision support for managers. At present, although statistical models can be used to help managers make decisions, these methods can not deal with complex nonlinear problems with incomplete data. Neural network can summarize laws from unpredictable data, and then provide decision support for managers to solve complex problems.
Secondly, in order to reduce the cost of construction projects, we must optimize the allocation of engineering resources. However, at present, there is no mathematical model to analyze the influence of various factors such as design changes and equipment conditions, and it is difficult to help managers rationally allocate construction resources. Neural network can predict resources and determine the priority of resources, and then help managers optimize resource allocation. In addition, neural network can also be used to complete the comprehensive analysis of existing data and information, and then help managers choose building materials, equipment and construction methods.
In a word, in the aspect of building economic management, neural network can be applied to project cost prediction, risk early warning and bid quotation determination, and has achieved good application results. But at present, the existing neural network system has not been further improved. Therefore, in order to apply neural network to solve the problem of building economic management, it is necessary to further improve the research of related neural network systems.
The application of the above neural network in construction economy was collected and sorted by Zhong Da Consulting Company.
For more information about project/service/procurement bidding, and to improve the winning rate, please click on the bottom of official website Customer Service for free consultation:/#/? source=bdzd