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Traditionally, temperature control for massive concrete structures during the construction phase is based on experience instead of using advanced methods such as predictive control method. In this paper, a predictive control method with a hybrid data-based artificial neural network (ANN) model is proposed. The main parameters of the numerical model are identified based on the data measured from the site. Since the limitation of the measured data, a rich dataset is generated by the numerical model with the identified parameters. Finally, a hybrid data-based ANN model for predicting the temperature indicator of massive concrete is developed. A case study in which the temperature control of concrete in a real bridge anchorage foundation construction in Southwestern China is conducted. Results show that the maximum temperature of concrete can be controlled around the target temperature by the proposed method. The average temperature decline rate decreases from 0.19 °C/h to 0.14 °C/h in the period of 120 h (i.e., from 60 h to 180 h after the concrete pouring) at a sudden decrease of 8 °C in environmental temperature, thereby enabling a significant reduction in the risk of concrete cracking. [ABSTRACT FROM AUTHOR]

Current housing unit design focuses on performance optimization, such as building energy use, daylighting, and occupant comfort. There is significant potential for efficient optimization in the early design stage. This paper proposed a building energy demand optimization framework that considers building design variables for housing unit design in Beijing. First, a high-rise apartment building was designed using geometric and thermal envelope parameters. Second, a thermal load simulation of the housing unit was performed. Third, sensitivity analysis and multi-objective optimization were conducted to evaluate the building's performance. Finally, a neural network was trained to predict the thermal load. A comparison of the base case and optimal cases revealed that more than 20% of energy demand could be saved. In addition, the effect of the parameters on the thermal load and the lighting schedules on the unit's energy load were analyzed. [ABSTRACT FROM AUTHOR]

Partner selection is a fundamental issue in supply chain management as it contributes significantly to overall supply chain performance. However, such decision-making is problematic due to the need to consider both tangible and intangible factors, which cause vagueness, ambiguity and complexity. This paper proposes a new fuzzy intelligent approach for partner selection in agile supply chains by using fuzzy set theory in combination with radial basis function artificial neural network. Using these two approaches in combination enables the model to classify potential partners in the qualification phase of partner selection efficiently and effectively using very large amounts of both qualitative and quantitative data. The paper includes a worked empirical application of the model with data from 84 representative companies within the Chinese electrical components and equipment industry, to demonstrate its suitability for helping organisational decision-makers in partner selection. [ABSTRACT FROM AUTHOR]

In this study, a supervised artificial neural network (ANN) trained by back propagation (BP) algorithms was developed to predict the transpiration of poplar based on six input variables. Based on the transpiration characteristics of trees, a three-layer BP network was constructed with six input units and one output unit, Daily average temperature, relative humidity, photosynthetic affective radiation, wind speed, sunlight duration and the soil water content 50 cm below the soil surface were considered as the six input variables of the network, which primarily affected the transpiration of poplar. The prediction of daily transpiration of poplar in Heilonggang region, Hebei province was conducted. The research results indicated that R² equalled 0.9534 between measured values and predicted values. The maximum relative error, the minimum relative error and the average relative error were 16.85, 1.49, and 4.2%, respectively. The proposed model could describe the relationship between the daily transpiration of poplar, the meteorological factors and soil moisture conditions with a relatively high accuracy. The research results had potential values for the production and management in this polar stand. [ABSTRACT FROM AUTHOR]

Accurate electricity consumption forecasting (ECF) is challenging due to its complexity, and it is more challenging for a provincial ECF in China when it comes to the great heterogeneity among provinces. To depict and predict electricity consumption, a new framework, the panel quantile regression neural network (PQRNN), is developed by adding an artificial neural network structure to a panel quantile regression model. The PQRNN can account for the complex nonlinear relationship and the latent provincial heterogeneity simultaneously. In addition, a differential approximation of the quantile loss function and a quasi-Newton optimization based on the backpropagation algorithm is developed. The prediction accuracy is evaluated by an empirical analysis of the provincial panel dataset from 1999 to 2017 in China, which shows that the ECF based on the PQRNN performs well. Finally, the provincial electricity consumptions over the next 5 years (2018–2022) are predicted by utilizing the PQRNN model. [ABSTRACT FROM AUTHOR]

Objectives: This study was conducted to estimate road traffic deaths and to forecast short-term road traffic deaths in China using the Elman recurrent neural network (ERNN) model.Methods: An ERNN model was developed using reported police data of road traffic deaths in China from 2000 to 2017. Different numbers of neurons of the hidden layer were tested and different combinations of subgroup datasets have been used to develop the optimal ERNN model after normalization. The mean absolute error (MAE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE) were measures of the deviation between predicted and observed values. Predicted road traffic deaths from the ERNN model and the seasonal autoregressive integrated moving average (SARIMA) model were compared using the MAPE.Results: By comparing the MAE, RMSE and MAPE of different numbers of hidden neurons and different ERNN models, the ERNN model provided the best result when the input neurons were set to 3 and hidden neurons were set to 10. The best validated neural model (3:10:1) was further applied to make predictions for the latest 12 months of deaths (MAPE = 4.83). The best SARIMA (0, 1, 1) (0, 1, 1)12 model was selected from various candidate models (MAPE = 5.04). The fitted road traffic deaths using the two selected models matched closely with the observed deaths from 2000 to 2016. The ERNN models performed better than the SARIMA model in terms of prediction of 2017 deaths.Conclusions: Our results suggest that the ERNN model could be utilized to model and forecast the short-term trends accurately and to evaluate the impact of traffic safety programs when applied to historical road traffic deaths data. Forecasting traffic crash deaths will provide useful information to measure burden of road traffic injuries in China. [ABSTRACT FROM AUTHOR]

Since a river system is a unidirectional network, the upstream influencing factors often interfere with those downstream. We quantify the effects of the TGD on the sediment exchange processes between the Yangtze River and Dongting Lake. Based on yearly sediment load data from 1981 to 2008, two multi-layer perceptron (MLP) models were constructed to predict the potential sedimentation in Dongting Lake without implementation of the TGD. The sediment discharge at Yichang station decreased from 622.5 to 61.1 Mt/year between 1981–1985 and 2003–2008, while the sedimentation in Dongting Lake reduced from 127.3 to 6.6 Mt/year. The MLP models indicate that only 27.9% of the decreased sediment load at Yichang station and 16.9% of that in Dongting Lake is caused by the TGD, while the rest is caused mostly by other upstream climate variations and anthropogenic impacts.EDITOR A. Castellarin; ASSOCIATE EDITORN. Ilich [ABSTRACT FROM PUBLISHER]