泉州师范学院学报
泉州師範學院學報
천주사범학원학보
Journal of Quanzhou Normal College
2015年
2期
62~65
,共null页
BP神经网络 建筑物沉降 沉降预测 泉州市
BP神經網絡 建築物沉降 沉降預測 泉州市
BP신경망락 건축물침강 침강예측 천주시
BP neural network ;building settlement ;settlement prediction; Quanzhou city
利用人工神经网络强大的学习能力,提出了基于BP人工神经网络的建筑物沉降预测方法.以泉州市东海湾某实例工程1~12期的沉降观测数据为基础,建立网络模型.将13~16期建筑物沉降的实测数据和模型的预测数据进行对比,发现两者间的误差相对较小,证明BP神经网络预测模型具有较高的精确性和稳定性,且具有一定的工程应用价值.
利用人工神經網絡彊大的學習能力,提齣瞭基于BP人工神經網絡的建築物沉降預測方法.以泉州市東海灣某實例工程1~12期的沉降觀測數據為基礎,建立網絡模型.將13~16期建築物沉降的實測數據和模型的預測數據進行對比,髮現兩者間的誤差相對較小,證明BP神經網絡預測模型具有較高的精確性和穩定性,且具有一定的工程應用價值.
이용인공신경망락강대적학습능력,제출료기우BP인공신경망락적건축물침강예측방법.이천주시동해만모실례공정1~12기적침강관측수거위기출,건립망락모형.장13~16기건축물침강적실측수거화모형적예측수거진행대비,발현량자간적오차상대교소,증명BP신경망락예측모형구유교고적정학성화은정성,차구유일정적공정응용개치.
Here we proposed a prediction method for the settlement of buildings based on BP artificial neural network with its strong nonlinear mapping and learning ability. In this paper we built a net- work model with the settlement observation data from phase 1 to 12 of a construction project in Quanzhou City as a foundation, and compared the values actually observed with those we predicted from phase 13 to 18,and the error between the two was relatively small.The results proved the high accuracy and stability of BP neural network prediction model and is reasonably reliable.
