Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

Wang

et al. 2021

Water

Accurate simulation of pollution load at basin scale is very important for controlling pollution. Although data-driven models are increasingly popular in water environment studies, they are not extensively utilized in the simulation of pollution load at basin scale. In this paper, we developed a data-driven model based on Long-Short Term Memory (LSTM)-Back Propagation (BP) spatiotemporal combination. The model comprises several time simulators based on LSTM and a spatial combiner based on BP. The time series of the daily pollution load in the Zhouhe River basin during the period from 2006 to 2017 were simulated using the developed model, the BP model, the LSTM model and the Soil and Water Assessment Tool (SWAT) model, independently. Results showed that the spatial correlation (i.e., Pearson’s correlation coefficient is larger than 0.5) supports using a single model to simulate the pollution load at all sub-basins, rather than using independent models for each sub-basin. Comparison of the LSTM-BP spatiotemporal combination model with the BP, LSTM and SWAT models showed that the performance of the LSTM model is better than that of the BP model and the LSTM model can obtain comparable performance with the SWAT model in most cases, whereas the performance of the LSTM-BP spatiotemporal combination model is much better than that of the LSTM and SWAT models. Although the variation of the simulated pollution load with the LSTM-BP model is high under different hydrological periods and precipitation intensities, the LSTM-BP model can track the temporal variation trend of pollution load accurately (i.e., the RMSE is 6.27, NSE is 0.86 and BIAS is 19.46 for the NH3 load and the RMSE is 20.27, NSE is 0.71 and BIAS 36.87 is for the TN load). The results of this study demonstrate the applicability of data-driven models, especially the LSTM-BP model, in the simulation of pollution load at basin scale.

Section: Bp-based Spatial Combinatorymentioning

confidence: 99%

“…Long Short-Term Memory (LSTM)-based temporal simulator.2.5.2. BP-Based Spatial CombinatoryUsing the nonlinear expression ability of the BP model[31], a spatial combiner based on BP was constructed to describe the spatial relationships among the sub-basins auto-…”

mentioning

confidence: 99%

Simulation of Pollution Load at Basin Scale Based on LSTM-BP Spatiotemporal Combination Model

Wang

et al. 2021

Water

“…e ratio of training set to test set might affect the prediction result. At present, the ratio of common training set and test set is 90% : 10%, 80% : 20%, or 70% : 30% [39,40].…”

Section: Case Analysismentioning

confidence: 99%

The Prediction of Metro Shield Construction Cost Based on a Backpropagation Neural Network Improved by Quantum Particle Swarm Optimization

Advances in Civil Engineering

Liu²,

et al. 2020

Self Cite

The prediction of construction cost of metro shield engineering is of great significance to project management. In this study, we used the rough set theory, a backpropagation (BP) neural network, and quantum particle swarm optimization (QPSO) to establish a prediction model for predicting the metro shield construction costs. The model accounts for the complexity of metro shield construction and the nonlinear relationship between the construction cost factors. First, the factors affecting the construction cost were determined by referring to the Chinese National Standards and analysing the engineering practice of typical metro shield projects. The rough set theory was used to simplify the system of influencing factors to extract the dominant influencing factors and reduce the number of input variables in the BP neural network. Since the BP neural network easily falls into a local minimum and has a slow convergence speed, QPSO was used to optimize the weights and thresholds of the BP neural network. This method combined the strong nonlinear analysis capabilities of the BP and the global search capabilities of the QPSO. Finally, we selected 50 projects in China for a case analysis. The results showed the dominant factors affecting the construction cost of these projects included ten indicators, such as the type of tunnelling machine and the geological characteristics. The determination coefficient, mean absolute percentage error, root mean square error, and mean absolute error, which are frequently used error analysis tools, were used to analyse the calculation errors of different models (the proposed model, a multiple regression method, a traditional BP model, a BP model optimized by the genetic algorithm, and the BP model optimized by the particle swarm optimization). The results showed that the proposed method had the highest prediction accuracy and stability, demonstrating the effectiveness and excellent performance of this proposed method.

“…It is mainly because the grey Verhulst model lacked the ability of self-learning and correcting the error [47,48]. Nowadays, ANN has been used in various fields of engineer-ing and plays an important role in predicting and distinguishing, while the BP neural network is one of the most widely used ANN in engineering fields for its strong ability of self-learning, information processing, nonlinear mapping, error feedback adjustment, and fault tolerance [49,50]. Though the BP neural network has such advantages in predicting foundation pit settlements, it still has limitation in optimizing weights and thresholds, for easily falling into the local optimum [51,52], while the genetic algorithm, which can be obtained by the near-optimal solutions in every search space, can well solve these problems [53,54].…”

Section: Introductionmentioning

confidence: 99%

Application of GA-BP Neural Network Optimized by Grey Verhulst Model around Settlement Prediction of Foundation Pit

Wang

et al. 2021

Geofluids

Due to the limitation in the prediction of the foundation pit settlement, this paper proposed a new methodology which takes advantage of the grey Verhulst model and a genetic algorithm. In the previous study, excavation times are often the only factor to predict the settlement, which is mainly because the correspondence between real-time excavation depth and the excavation time is hard to determine. To solve this issue, the supporting times are precisely recorded and the excavation depth rate can be obtained through the excavation time length and excavation depth between two adjacent supports. After the correspondence between real-time excavation depth and the excavation time is obtained, the internal friction angle, cohesion, bulk density, Poisson’s ratio, void ratio, water level changes, permeability coefficient, number of supports, and excavation depth, which can influence the settlement, are taken to be considered in this study. For the application of the methodology, the settlement monitoring point of D4, which is near the bridge pier of the highway, is studied in this paper. The predicted values of the BP neural network, GA-BP neural network, BP neural network optimized by the grey Verhulst model, and GA-BP neural network optimized by the grey Verhulst model are detailed compared with the measured values. And the evaluation indexes of RMSE, MAE, MSE, MAPE, and R 2 are calculated for these models. The results show that the grey Verhulst model can greatly improve the consistency between predicted values and measured values, while the accuracy and resolution is still low. The genetic algorithm (GA) can greatly improve the accuracy of the predicted values, while the GA-BP neural network shows low reflection to the fluctuation of measured values. The GA-BP neural network optimized by the grey Verhulst model, which has taken the advantages of GA and the grey Verhulst model, has extremely high accuracy and well consistency with the measured values.