Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network
المؤلفون المشاركون
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-01
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs.
To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work.
To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis.
The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems.
In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated.
According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used.
The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%.
However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger.
Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Peng, Hao& Wu, Han& Wang, Junwu. 2020. Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1124704
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Peng, Hao…[et al.]. Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network. Advances in Civil Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1124704
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Peng, Hao& Wu, Han& Wang, Junwu. Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1124704
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1124704
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر