水利与建筑工程学报
水利與建築工程學報
수리여건축공정학보
TECHNIQUE OF SEEPAGE CONTROL
2014年
5期
129-132
,共4页
填海造地%软土地基%堆载预压%GM(1 ,1)灰色模型%沉降预测
填海造地%軟土地基%堆載預壓%GM(1 ,1)灰色模型%沉降預測
전해조지%연토지기%퇴재예압%GM(1 ,1)회색모형%침강예측
reclamation%soft soil foundation%preloading%GM(1,1 ) grey model%settlement prediction
为了研究不同样本数据序列GM(1,1)灰色模型在填海造地道路软基沉降预测中的实用性和有效性。结合工程实例,以软基沉降监测数据为依据,分别选取堆载预压恒载期的10组和20组实测地基沉降数据作为样本数据序列,建立了相应的GM(1,1)灰色预测模型,对软土地基固结沉降进行了预测,并将两种不同数据序列灰色模型预测结果与现场实测数据进行了对比分析。研究结果表明:GM(1,1)灰色模型所得预测曲线与实测曲线变化趋势基本一致,预测值与实测值吻合较好,实测曲线比预测曲线收敛较快,较多样本数据序列灰色模型所得预测精度更高。
為瞭研究不同樣本數據序列GM(1,1)灰色模型在填海造地道路軟基沉降預測中的實用性和有效性。結閤工程實例,以軟基沉降鑑測數據為依據,分彆選取堆載預壓恆載期的10組和20組實測地基沉降數據作為樣本數據序列,建立瞭相應的GM(1,1)灰色預測模型,對軟土地基固結沉降進行瞭預測,併將兩種不同數據序列灰色模型預測結果與現場實測數據進行瞭對比分析。研究結果錶明:GM(1,1)灰色模型所得預測麯線與實測麯線變化趨勢基本一緻,預測值與實測值吻閤較好,實測麯線比預測麯線收斂較快,較多樣本數據序列灰色模型所得預測精度更高。
위료연구불동양본수거서렬GM(1,1)회색모형재전해조지도로연기침강예측중적실용성화유효성。결합공정실례,이연기침강감측수거위의거,분별선취퇴재예압항재기적10조화20조실측지기침강수거작위양본수거서렬,건립료상응적GM(1,1)회색예측모형,대연토지기고결침강진행료예측,병장량충불동수거서렬회색모형예측결과여현장실측수거진행료대비분석。연구결과표명:GM(1,1)회색모형소득예측곡선여실측곡선변화추세기본일치,예측치여실측치문합교호,실측곡선비예측곡선수렴교쾌,교다양본수거서렬회색모형소득예측정도경고。
In order to study the practicability and effectiveness of the grey models of different sample data sequence GM (1 ,1 ) in the road soft foundation settlement prediction for reclamation engineering ,the GM (1 ,1 ) grey model was estab-lished based on the soft foundation settlement monitoring data of an engineering project .In the test ,10 groups and 20 groups of measured settlement data during preloading constant period were selected as the sample data sequence ,accord-ing to which the corresponding GM (1 ,1 ) grey forecast model was established .The consolidation settlement of soft soil foundation was predicted with this model ,and the comparative analysis with the prediction results of two different data se-quence grey models and measured data of settlement monitoring was carried out .The results indicate that the changing trend of the predicted curve of GM (1 ,1 ) grey model is consistent with the measured curve ,the predicted values and the measured values are well accorded ,the measured curve convergence is faster than the predicted curve ,and more sample data sequence grey models lead to higher prediction accuracy .