Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation.

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A novel method for sensor fault diagnosis based on support vector machine (SVM) prediction model was proposed. This paper put forward the principle of SVM condtruction process and the system parameters obtained from using dynamic model identification of sensor. The sensor fault was diagnosed on line by prediction model, which avoided that BP algorithm must have mass data and is likely to fall into local minimum point. Compared to the traditional motheds, it was much more effective and accurate.

In traditional control field, the control system dynamic model is accurate or not affecting the merits of the main control key, the more detailed dynamic information system, the more it can achieve precise control. However, for complex systems, because too many variables are often difficult to accurately describe the dynamic system, so engineers will use a variety of methods to simplify the system dynamics, in order to achieve the purpose of control, but not ideal. Fuzzy control is essentially a nonlinear control, intelligent control of subordinate category. The design, select AT89S51 microcontroller as controller design an intelligent heat faster. The intelligent heater temperature can be maintained within a set temperature range, so that the water temperature to fluctuate within a range set value and set value down to 0.5 degrees Celsius. Temperature sensor detects a temperature every second. Temperature can be displayed to one decimal place. Temperature detected by real-time digital display. Fuzzy Control of a major feature of both systematic theory, there are a lot of practical application background. Development of fuzzy control has been rapid and widespread application. Nearly 20 years, both in theory and fuzzy control technology has made ​​great strides to become a very active field of automatic control but fruitful branch. Typical applications involve many aspects of their production and life, for example, fuzzy washing machines, air conditioners and other aspects.

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