At present, the field of construction engineering is limited by various situations, such as complex construction environments and many uncertain factors. Therefore, on the basis of the engineering network diagram, this paper proposes a construction project schedule management method based on the fuzzy logic neural network algorithm. By building a neural network, a large amount of historical data is input, and computers are allowed to calculate the key routes, thus predicting the construction period, and a construction project in a city is taken as an example for simulation experiments. The traditional construction period management scheme expects a construction period of 55 days. The planned construction period optimized by the project management technology integrating fuzzy logic neural network algorithm is 55 days, which is 2 days less than the traditional construction project schedule management technology and will not cause construction period delay. The simulation results show that this algorithm is more accurate and more efficient in calculating the key lines when dealing with large-scale projects, which can help the construction unit to quickly find the optimal strategy and effectively reduce the construction delay and capital loss caused by uncertainty factors.
The current methods proposed in construction cost control of construction projects have problems such as applicability and low accuracy. Therefore, this paper proposes a method for evaluating engineering cost control based on the big data Analytic Hierarchy Process (AHP) method. Based on the big data technology, the construction cost control index of the construction project is selected, and the construction cost control index model of the construction project is constructed according to the selection results, and the evaluation set is constructed. The corresponding weights of the construction cost control indicators of each construction project are obtained through the analytic hierarchy process, and a weight set is constructed. The membership relationship between the index set and the evaluation set is determined, a fuzzy relationship matrix is constructed, and the construction cost control result of the construction project is obtained according to the calculated weight and the constructed fuzzy relationship matrix. The test results of examples show that the risk assessment accuracy of the proposed method is higher than that of ordinary methods, and the quantitative processing of audit risk assessment is realized.
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