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黄土高填方场地工后沉降预测模型性能评估方法

于永堂, 郑建国, 孙茉, 黄鑫, 韩文斌

于永堂,郑建国,孙茉,等. 黄土高填方场地工后沉降预测模型性能评估方法[J]. 中国地质灾害与防治学报,2023,34(4): 39-48. DOI: 10.16031/j.cnki.issn.1003-8035.202211003
引用本文: 于永堂,郑建国,孙茉,等. 黄土高填方场地工后沉降预测模型性能评估方法[J]. 中国地质灾害与防治学报,2023,34(4): 39-48. DOI: 10.16031/j.cnki.issn.1003-8035.202211003
YU Yongtang,ZHENG Jianguo,SUN Mo,et al. Evaluation methods for performance of post-construction settlement prediction models in thick loess filled ground[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4): 39-48. DOI: 10.16031/j.cnki.issn.1003-8035.202211003
Citation: YU Yongtang,ZHENG Jianguo,SUN Mo,et al. Evaluation methods for performance of post-construction settlement prediction models in thick loess filled ground[J]. The Chinese Journal of Geological Hazard and Control,2023,34(4): 39-48. DOI: 10.16031/j.cnki.issn.1003-8035.202211003

黄土高填方场地工后沉降预测模型性能评估方法

基金项目: 陕西省秦创原“科学家+工程师”队伍建设项目(2022KXJ-086);陕西省技术创新引导专项(基金)计划项目(2020CGHJ-002);国家自然科学基金项目(42072302)
详细信息
    作者简介:

    于永堂(1983-),男,辽宁鞍山人,博士,正高级工程师,主要从事岩土工程测试与监测技术、特殊土工程性质评价与地基处理技术的研发与应用。E-mail:yuyongtang@126.com

  • 中图分类号: TU444

Evaluation methods for performance of post-construction settlement prediction models in thick loess filled ground

  • 摘要: 工后沉降预测结果是黄土高填方场地变形稳定性评价和建筑物规划布局的重要参考依据。为遴选适合黄土高填方场地的工后沉降预测模型,基于某典型黄土高填方工程的实测沉降数据,分析了工后沉降曲线的变化规律和发展趋势,建立了17种回归参数模型,提出了模型预测效果的评价指标和方法。结果表明:(1)该工程填方区工后沉降历时曲线呈“缓变型”变化,土方填筑完工初期无陡增段,随时间增加沉降速率逐步降低,尚未出现沉降趋于稳定的水平段;(2)将外推预测误差、内拟合误差和后验误差比最小化作为综合控制目标,可遴选出理想的回归参数模型;(3)MMF模型(Ⅱ型)和双曲线模型具有较高的预测精度、较好的稳定性和较强的适应性,在17种模型中的预测效果最佳;(4)沉降数据的变化越平稳,模型预测效果越好;(5)增大建模数据的时间跨度,会提升预测精度,但增大至一定值后,预测精度提升效果不再显著。
    Abstract: The prediction of post-construction settlement is an important reference for the evaluation of deformation stability evaluation and building layout planning in thick loess filled ground. To choose suitable models for predicting post-construction settlement in thick loess filled grounds, the characteristics of post-construction settlement curves are analyzed based on the measured settlement of a thick loess fill ground project. Seventeen regression parameter models are established, and some evaluation indexes and methods for models are proposed. The best prediction models for post-construction settlement prediction are optimized. The results indicate that the post-construction settlement curves of the filling area change slowly, with no steep increase in the initial stage of earthwork filling. The settlement rate gradually decreases with time, and there is no horizontal section where the settlement tends to be stable. The optimal regression parameter model can be selected by minimizing the extrapolation prediction error, the internal fitting error, and the posteriori error ratio as the comprehensive control objective. The MMF model (TypeⅡ) and hyperbolic model show high prediction accuracy, good stability, and strong adaptability, with the prediction effect being the best among the 17 models. The more stable the settlement data changes, the better the model prediction effect. Increasing the time span of modeling data would improve the prediction accuracy, but the improvement effect on prediction accuracy would no longer be significant after reaching a certain value.
  • 泥石流作为常见的山区地质灾害[1],因洪流中夹杂着巨量砂石,且暴发突然,对山区人民的生产和生活造成巨大威胁。尤其在震后,由于地壳运动引发山体破坏,导致碎石垮塌、树木根部受损,地震引起的大量滑坡、崩塌产生了丰富的松散物源,导致震后较震前引发泥石流的可能性更大[2]。除此之外,随着我国工程技术的突飞猛进,西部山区的开发及“一带一路”建设如火如荼的进行,在指定场地集中堆放项目施工过程中产生的大量级配宽、体量大的弃土弃渣,也产生了大量的固体物质,在强降雨或上游来水的动力激发下,极易造成研究区水土流失,进而破坏周边各类基础措施、威胁人民生命财产安全[3]、阻碍社会经济发展与技术进步,对人类生存造成严重影响[4]

    彝海镇在强对流天气作用下,2020年6月26日晚21时—27日凌晨3时安宁河流域内的彝海镇大马乌村和大堡子村出现历史上罕见的突发性特大暴雨。安宁河起源于四川省凉山州冕宁县彝海镇东侧的山谷内,其上游河道较窄,水位快速上涨,特大暴雨使安宁河冲毁原来的河道,洪水携带着大量的石块和树干冲毁了大量房屋、农作物及基础设施等(图1)。大堡子村在7h内降雨量可达145.7 mm,大马乌村降雨量高达197.5 mm。泥石流巨大的冲击力造成彝海镇及高阳街道近万人受灾,其中16人遇难、6人失踪。另外倒塌房屋670余间;因灾受损农作物上千ha;堤防、道路、电力线路受损严重,累计超50km;损坏便桥6座。直接经济损失高达7.4亿元[5]

    图  1  泥石流灾害情况
    Figure  1.  Debris flow disaster situation

    位于四川省凉山彝族自治州冕宁县境东南部彝海镇安宁河畔的大马乌村和大堡子村(图2),东邻越西县板桥乡、中所镇,西壤大桥镇,南接高阳街道,北连石棉县栗子坪[6]

    图  2  泥石流研究区位置
    Figure  2.  Location of debris flow study area

    彝海镇隶属四川凉山彝族自治州冕宁县,域内地势悬殊,高海拔地区常年日照充足,年均气温变化幅度小,而早晚温差较大,具有干湿分明寒带气候特点;低海拔地区表现为冬暖夏凉、季节变化不明显等亚热带气候特征[7]。研究区境内气候具有小范围差异:东部平原地区处于安宁河流域,因西南季风沿安宁河北上的暖湿气流,安宁河流域平原区具有利于农业发展的半湿润且温暖较好气候条件;北部海拔较高,气候较为湿润;西部雅砻江高山峡谷区,因地形高差悬殊,气候呈垂直分布,存在江边亚热带气候及山顶寒温带气候。

    冕宁县降雨情况及蒸发量因地貌不同存在差异。东部、北部及山谷地区年均降雨量丰富,而西部、南部及河谷地带常年干旱。湿气在北部高山拦阻下形成雨屏,因此北部地区不易蒸发。彝海镇处于冕宁县北部,其降水量丰富,年均降雨量超1200 mm,见图3(a),日最大164 mm,小时最大150 mm。在年降雨中,大多为夜间降雨,超全年总降雨量7成[8]

    图  3  研究区基本概况[8]
    Figure  3.  Basic situation in research area

    冕宁县地处青藏高原东缘,面朝雅砻江[9],地势总体呈南低北高,全区总面积90%为山地地貌,地形起伏较大,最大高差悬殊达4 km,研究区处于2500 ~ 3000 m高程处,见图3(b)。在南北向地质构造的影响下,北西—南东走向的雪山在进入冕宁县域后呈树枝状形式,由西到东,分为3条并列的南北走向山脉,且在3山间夹着两条南北向的径流,总体展布为锦屏山、雅砻江、牦牛山、安宁河、小相岭等三山夹两水的地势形式。研究区内地质构造与康滇构造体系斜接,归属于川滇构造体系,其断裂主要是因东西向存在挤压应力,而形成的南北向的断裂和褶皱。

    冕宁县地层以出露较齐全、厚度大和岩石成岩时代多为特征,总厚度9 567 m左右,出露地层主要包括第四纪松散堆积物(Q)、二叠系(P)、三叠系(T)和震旦系(Z),且区内侵入岩较为发育,这种岩性比较松散,更容易发生泥石流。

    此次泥石流是由局地突发性强降雨引发,其容重及黏粒成分直接决定泥石流搬运能力,经现场勘察得知,泥石流在搬运中近9成为尺寸较小的石块(图4)。通过室内颗粒级配实验,分析研究区保存完整的细颗粒泥石流沉积物的黏粒(粒径小于5×10−3 mm)成分[10]得知,此次泥石流的黏粒含量在5.0%~10.5%。根据相关勘查技术教材[11]及技术规范[12]计算泥石流容重,选择计算公式如式(1)。

    图  4  沟道中碎石
    Figure  4.  Gravel in the ditch
    γc=lg[10x+0.23|x0.089|+0.1]+e20x1+1.1 (1)

    式中:γc——泥石流容重/(g·cm−3);

    x—泥石流沉积物中的粒径小于5×10−3 mm的 黏粒含量。

    经过计算可知泥石流容重在2.15 g/cm3左右。

    利用形态调查法[13],在安宁河沟下游村庄里,进行测量流经村庄的泥痕高度(图5)、安宁河沟沟床纵比降及泥石流过流断面面积等,从而计算泥石流峰值流量[14],计算公式如式(2)。

    图  5  流经村庄泥痕
    Figure  5.  Mud mark in the village
    Q=WVV (2)

    式中:Q——泥石流峰值流量/(m3·s−1);

    WV——泥石流过流断面面积/m2

    V——泥石流断面平均流速/(m·s−1)。

    通过式(3)计算:

    V=(1nc)Hc2/3Ic1/2 (3)

    式中:n—泥石流沟的沟床糙率(石块粒径大多在是、 10 cm左右,挟有个别2~3 m的大石块, 0.5 m<Hc<2 m),在此取为0.077;

    Hc——泥深/m;

    I——泥石流沟床比降/‰(约150‰)。

    经过计算可知本次泥石流水流经安宁河沟口流速为2.69 m/s,峰值流量为171.21 m3/s,冲出量为14.3×104 m3[15],而安宁河沟口流量设计峰值为96.80 m3/s,流速为2.69 m/s,冲出量为9.77×104 m3

    地形陡峭、地势高差悬殊的地区,经过长期风化作用,陡坡上岩土体碎裂严重,为泥石流的发育提供大量松散固体物源,除此之外,岩土工程施工及过度开采也会产生大量废渣,在突发性暴雨作用下,极易形成泥石流。因此,泥石流暴发的高峰期一般在特定地形地质条件地区的汛期(冕宁县主要集中在6—9月,见图6)。彝海镇泥石流的发生主要受地形地貌条件、人类不规范工程活动、突发性强降雨等影响。

    图  6  冕宁县地质灾害发生分布月份
    Figure  6.  Distribution months of geological disasters in Mianning County

    特定的地形有利于提供充足的水动力及松散物源。根据曹晨等[16]及余斌等[17], 25° ~ 45°的岸坡坡度是引发泥石流的最佳条件。通过收集当地年鉴及勘察可知,安宁河流域在2008年之后历经多次地震,沟谷呈深V切割状,流域属高山侵蚀地貌,两侧岸坡坡度30°左右(图7)。岸坡上破碎岩体势能较大,流域内曾多次发生崩滑,上游沟床纵比降较大(100‰~300‰),岸坡坡脚处在沟道径流强烈冲刷下,沟床内产生大量固体物质。在强降雨作用下沟谷间容易形成汇流,这将提供充足的水源动力,雨水储存在沟道形成区,增加岸坡堆积物孔隙水压力,破坏了岩土体的静态平衡,产生大量的松散物源。在经过多次地震后,沟道出现淤积和堵塞,导致泥石流形成区扩大。

    图  7  安宁河沟流域示意图
    Figure  7.  Schematic diagram of the Anning River valley

    另外,因流域上游可形成汇流的各支沟沟床纵比降、沟长及沟道形态不同,流域内水动力条件及输沙能力提高,易形成洪峰。因此,安宁河沟的地形条件是泥石流发生的主要原因之一。

    人类的频繁活动改变了当地土体强度,因建筑施工,产生了大量的工业废料,在大雨的冲刷下易形成泥石流。冕宁县处于高海拔山区,县域面积4422.742 km2,人口近32万人,人口分布不均,河谷地带居民居多,而山地地区较少,乡镇、街道错落在安宁河两岸开阔的台地或坡地。

    多年来,受历史及地理位置的限制,区内经济交通、工业化较落后,以农业为主要的经济收入。其主要工程活动为:农业活动、水电站的开发及应用、修房筑路以及矿产资源开采等。如今,随着生产能力的提升,县境内实现“路路通”,因缺乏专业的技术支持,筑路时的不合理削坡,导致区域内形成大量的人工边坡,且大多数缺乏有效的工程防护结构,安全系数较低。因此,安宁河流域附近的不规范工程活动是泥石流发生的主要原因之一。

    丰富的松散物源可以在充足的水动力下形成泥石流灾害。结合地面调查、遥感分析、资料收集整理得知,安宁河沟上游岸坡,在地震作用及长久的风化作用下,产生了大量的破碎岩体,且局部塌落,上游沟道内出现淤积和堵塞,导致泥石流形成区不断扩大。在2008年后,冕宁县共发生137次滑坡地质灾害,其中安宁河流域高达47次,除此之外,因交通不便,当地村民在进行工程活动后产生的大量工业废料未能及时运出,选择堆积在安宁河沟中下游附近,部分崩滑松散物源及工业废料,在强降雨和泥石流的冲刷下在沟道内形成近40万m3的堆积物源,其中动储量超10万m3,在泥石流流通区,沟道中松散物质持续补给泥石流,山洪携带的泥沙物质随之增多,增大了泥石流的规模和破坏力,进而发生泥石流灾害。

    结合陈宁生等[18]如何判别泥石流流体性质的方式,按照容重指标得出“6·26”泥石流为大型中频黏性泥石流。区域内地震频发且人类工程活动及农业活动频繁,在安宁河沟道中产生大量崩滑松散物源及工业废料,在暴雨洪水的冲刷下形成堆积物源。由于沟谷纵坡大、下游地形陡峻、充沛的降雨(年均1 200 mm以上)等,泥石流在此地极易发育。

    综上,彝海镇泥石流成因如下:(1)陡峭的地形:安宁河沟沟谷呈深V切割状,地形陡峭,沟道纵比降较大;(2)丰富的松散物源:在地震频发及人类频繁的工业活动下破坏了流域内坡体及岩土体结构,区域内农业为主,居民退林还耕现象严重(图8),降低了土体的稳定性,增加了松散物源;(3)充足的水动力:“6·26”泥石流前7小时内,研究区遭遇突发性强降雨,超过了区域降雨阈值(安全临界降雨量)(图9),导致安宁河洪水泛滥,洪水携带着松散物源向下游冲刷,其流量大大超过了安宁河沟设计峰值流量,最终冲毁堤坝并发育为泥石流。

    图  8  退林还耕
    Figure  8.  Conversion of forest to farmland
    图  9  彝海镇不同监测点6月26日18:00至6月27日1:00累计降雨量
    Figure  9.  Cumulative rainfall from 18:00 on June 26 to 1:00 on June 27 at different monitoring points in Yihai Town

    灾害发生后,通过对受灾区进行详细勘察,发现研究区破坏严重的原因,主要是沟谷内居民房屋选址不佳、砖木结构房屋结构强度低;其次居民防灾知识薄弱、群测群防水平较低、逃生线路选择不当等。经过对安宁河流域详细勘察,沟道岸坡坡度大、地形陡峭,目前仍存在大量可参与泥石流活动的动储量(图10),“6·26”泥石流后处于相对稳定状态,但若再次遭遇突发性特大强降雨,将存在再次引发泥石流的可能。历经多次地震和强降雨,沟道内的松散物源有所增加,特别是崩滑作用形成的固体物质剧增,缩短了泥石流的发育周期。

    图  10  安宁河沟目前松散物源
    Figure  10.  Present loose source in the Anning River ditch

    泥石流严重影响人民的正常生活,基本主要进行拦挡、排泄工程治理。除此之外,对边坡做支护处理也是非常必要,降低为泥石流的发育提供松散物源的趋势。结合现场实地考察分析,得出建议方案为:(1)因当地居民的生产生活,安宁河沟道受损严重,沟道旁修建的土路密实性及抗冲刷能力较差,当遇到突发性暴雨时,沟道出现堵溃,洪水将增强泥石流规模,对下游居民造成严重损失。因此需要对整条沟道进行定期清淤工作,并且在合适位置修建排洪通道,以便遇到大暴雨时,可以高效排洪,降低泥石流对下游的伤害。(2)处理沟道中的桥墩,在外表面加装橡胶保护,提高其抗冲刷能力,以防河道被摧毁。(3)在岩土工程施工或开采资源工作中尽可能避开雨季,对安宁河流域的降雨情况实时监测,及时预警,以防灾害发生。(4)当地主要收入以种植农作物为主,导致土质疏松,稳定性较差,因此可以通过栽种树木的方式加固土壤,以便提高其稳定性,减少松散物源,降低泥石流发生的可能性[19]。(5)目前已建的防护堤被冲毁、淤满,需重修防护堤,且要加高防护安全高度。拟在泥石流堆积区沟道左岸重建防护堤,可提高沟道泥石流过流和泄洪能力及保护下游农作物。(6)采用挡土墙及抗滑桩等对未来工程建设产生的人工边坡进行防护。(7)原先居民房屋多为砖木结构,损失惨重,因此灾后重建要在安全地带建设钢混材料房屋及构筑生活区,以便提高抗震和抗冲刷能力。(8)提高群众安全意识,做好安全演练,提高居民群测群防水平。

    (1)2020年6月26日,冕宁县彝海镇发生大型泥石流,冲毁了河道、居民房屋及农田,严重威胁下游人员生命安全。判断此次是由局地强降雨造成的发生于夜间、激发时间短、持续时间长、造成损失惨重的暴雨沟谷大型中频黏性泥石流。经过形态调查法,得出泥石流容重在2.15 g/cm左右,流经安宁河沟口峰值流量为171.21 m3/s,冲出量为14.3×104 m3,严重超出了安宁河沟口设计峰值流量。

    (2)其主要成因为安宁河沟沟谷呈深切割状,地势陡峭,坡度及沟床纵比降较大,为泥石流发育提供了良好的自然条件。且区域内地震频发及人类活动频繁,所以该流域松散固体物质丰富,在特大暴雨下,引发大型泥石流。

    (3)6月26日暴发的大型泥石流,造成了重大人员伤亡及财产损失,主要原因为:①当地居民防灾减灾意识相对较为薄弱;②房屋选址不当(大部分房屋位于泥石流流通区)以及房屋建筑结构安全性较差;③泥石流灾害相关知识较为匮乏,灾害发生时不能正确选择逃生路线。

    (4)经过无人机勘察现场可知,此次泥石流冲出量为14.3万m3,仅占动储量的43%、流域物源总量的20.9%,安宁河沟道内仍有近50万 m3的松散物源,超27万m3动储量将再次引发泥石流,尤其在中、下游局部沟道堵塞情况较严重。若再次遇到突发性强降雨,将再次遭遇泥石流袭击,特别在未来5—10 a将频繁暴发。

  • 图  1   试验场地内的代表性地表沉降监测点平面示意图

    Figure  1.   Distribution map of representative surface settlement monitoring points of post-construction settlement within the test site

    图  2   试验场地内代表性监测点的工后沉降曲线

    Figure  2.   Post-construction settlement curve of representative monitoring points within the test site

    图  3   各模型的内拟合及外推预测曲线

    Figure  3.   Internal-fitting and extrapolation prediction curves of each model

    图  4   各模型对实测沉降数据的拟合及预测误差

    Figure  4.   Fitting and prediction errors of each model for measured settlement data

    图  5   优选模型在建模数据不同时间跨度时的拟合及预测曲线

    Figure  5.   Curve fitting and prediction results of the optimal model with different time span

    表  1   沉降预测中常用的回归参数预测模型

    Table  1   Summary of regression parameter prediction models for settlement prediction

    类型模型名称数学表达式沉降速率备 注




    Logistics^t=a/(1+bect)s^t=abcect/(1+bect)2收敛模型
    Gompertzs^t=aeebcts^t=aceebctebct收敛模型
    Ushers^t=a/(1+bect)ds^t=abcdect(1+bect)1d收敛模型
    Weibulls^t=a(1bectd)s^t=abcdectdt1+d收敛模型
    MMF-Ⅰs^t=(ab+ctd)/(b+td)s^t=cdtd1/(b+td)dtd1(ab+ctd)/(b+td)2收敛模型
    MMF-Ⅱs^t=atb/(c+tb)s^t=abtb1/(c+tb)abt2b1/(c+tb)2收敛模型
    Richardss^t=a(1bect)1/(1d)s^t=abcect(1bect)d/(1d)/(1d)收敛模型
    Knothe-Ⅰs^t=a(1ebtc)ds^t=abcdtc1ebtc(1ebtc)d1收敛模型
    Knothe-Ⅱs^t=a(1ebt)cs^t=abcebt(1ebt)c1收敛模型
    Bertalanffys^t=[a1/3(ab)1/3ect]3s^t=3(ab)1/3cect[a1/3(ab)1/3ect]2收敛模型
    邓英尔s^t=a/(1+bectd)s^t=abcdectdtd1/(1+bectd)2收敛模型




    Spillmans^t=a(ab)ects^t=(ab)cect收敛模型
    指数曲线s^t=a(1ebt)s^t=abebt收敛模型
    双曲线s^t=t/(a+bt)s^t=a/(a+bt)2收敛模型
    幂函数s^t=atbs^t=abtb1发散模型
    平方根函数s^t=a+bts^t=b/2t发散模型
    对数函数s^t=alnt+bs^t=a/t发散模型
    对数抛物线s^t=a(lgt)2+blgt+cs^t=lge(b+2algt)t1发散模型
    星野法s^t=s0+abtt01+b2(tt0)s^t=ab2[1+b2(tt0)](tt0)ab3tt02[1+b2(tt0)]3/2收敛模型
    下载: 导出CSV

    表  2   各模型对典型监测点S6实测数据的内拟合及外推预测结果

    Table  2   Internal-fitting and extrapolation prediction results of each model for the measured settlement data at a typical monitoring point S6

    类型模型名称模型参数内拟合效果外推预测效果后验误差
    Ci
    abcdMAPE* /%SSERMSER2MAPE /%MADMSEMFE




    Logistic39.75407.60800.02529.888.060.950.992821.6611.77179.3211.772.19
    Gompertz43.78540.93950.01506.103.090.590.997316.398.99111.118.992.69
    Usher64.0873−0.99310.0049−1.01495.162.720.520.99815.032.7410.972.310.98
    Weibull3.3377−52.224814.7577−0.42323.582.040.450.99863.091.653.871.210.86
    MMF-Ⅰ2.4888869.749868.87151.31993.261.710.440.99857.183.9321.543.622.20
    MMF-Ⅱ113.91600.9777329.46765.843.040.550.99792.051.051.770.410.35
    Richards51.88740.86870.00850.37243.181.660.430.99859.985.4942.785.253.13
    Knothe-Ⅰ41.09490.00004.06870.20984.492.170.490.998118.7510.31147.8010.314.18
    Knothe-Ⅱ68.6817240.20710.95625.652.870.560.99753.792.056.411.580.67
    Bertalanffy46.897236.19480.01164.452.030.470.998213.427.3976.867.323.02
    邓英尔3.2623−0.955828.7664−1.23683.241.810.420.99876.323.4516.623.111.95




    Spillman68.57270.70740.00434.502.470.520.99783.591.935.671.380.80
    指数曲线62.62260.00505.953.010.580.99735.713.1213.952.740.96
    双曲线3.12210.00956.003.060.550.99792.751.463.160.930.46
    幂函数0.64250.78524.345.380.730.996311.676.1542.57-6.152.69
    平方根函数−8.95053.374813.1814.001.250.98762.901.542.781.360.22
    对数函数11.6931−28.299026.75113.313.550.899324.2012.77182.5812.770.90
    对数抛物线21.4982−43.538525.86044.122.830.560.99752.751.462.831.070.67
    星野法1367.00000.001832.50114.393.380.920516.818.8084.058.800.52
    注:“—”表示无此参数。
    下载: 导出CSV

    表  3   优选模型对不同监测点沉降数据的内拟合和外推预测结果

    Table  3   Internal-fitting and extrapolation prediction of settlement data for different monitoring points using the optimal model

    监测点模型名称模型参数内拟合效果
    R2
    外推预测效果后验误差比
    Ci
    abcMAPE /%MFE
    S2双曲线15.82650.07470.98058.33−0.600.58
    MMF-Ⅱ7.49421.5515829.14000.98807.420.560.55
    S3双曲线4.96100.01450.99693.281.310.73
    MMF-Ⅱ64.77241.0199344.13450.99694.111.600.88
    S4双曲线3.44750.00990.99730.780.560.12
    MMF-Ⅱ90.25251.0341350.09080.99742.211.280.32
    S5双曲线3.76770.00950.99902.341.290.64
    MMF-Ⅱ86.20611.0584397.98120.99914.822.511.14
    S6双曲线3.12470.00950.99781.420.920.24
    MMF-Ⅱ112.90760.9804329.86600.99790.570.460.10
    S7双曲线4.42750.01080.99442.15−0.750.22
    MMF-Ⅱ71.24081.0794416.68110.99461.300.640.13
    S8双曲线9.80390.18990.941931.962.243.26
    MMF-Ⅱ−1.03960.1205−2.30300.98140.55−0.050.11
    S9双曲线7.94480.02880.98168.111.790.54
    MMF-Ⅱ35.32010.9936274.50820.99997.881.740.53
    S10双曲线5.52940.01670.99084.161.370.40
    MMF-Ⅱ166.37110.8423540.34700.99173.30−0.980.36
    S11双曲线4.00730.01000.99813.511.750.69
    MMF-Ⅱ143.15990.9305451.83950.99820.260.220.06
    S12双曲线3.08850.01010.99815.403.111.01
    MMF-Ⅱ139.22670.9179325.71700.99841.821.140.40
    S13双曲线3.65370.01040.99892.591.370.68
    MMF-Ⅱ145.90220.9138397.92440.99921.51−0.600.72
    S14双曲线4.42230.01230.99863.521.520.73
    MMF-Ⅱ74.27161.0255358.68420.99864.611.960.94
    S15双曲线3.81730.00930.99821.931.120.38
    MMF-Ⅱ77.34641.1049426.31570.99866.283.261.20
    S16双曲线3.39890.00840.99671.541.020.22
    MMF-Ⅱ160.00720.9428447.04790.99681.24−0.510.20
    S17双曲线3.27200.01100.99685.663.080.78
    MMF-Ⅱ100.13780.9706296.39210.99684.462.460.63
    注:“—”表示无此参数。
    下载: 导出CSV
  • [1] 高建中, 郑建国, 魏弋锋, 等. 延安新区黄土丘陵沟壑区域工程造地实践[M]. 北京: 中国建筑工业出版社, 2019

    GAO Jianzhong, ZHENG Jianguo, WEI Yifeng, et al. Engineering practice of land reclamation in loess hilly gully areas in Yan’an an new district[M]. Beijing: China Architecture & Building Press, 2019. (in Chinese with English abstract)

    [2] 朱才辉,李宁,刘明振,等. 吕梁机场黄土高填方地基工后沉降时空规律分析[J]. 岩土工程学报,2013,35(2):293 − 301. [ZHU Caihui,LI Ning,LIU Mingzhen,et al. Spatiotemporal laws of post-construction settlement of loess-filled foundation of LYUliang Airport[J]. Chinese Journal of Geotechnical Engineering,2013,35(2):293 − 301. (in Chinese with English abstract)

    ZHU Caihui, LI Ning, LIU Mingzhen, et al. Spatiotemporal laws of post-construction settlement of loess-filled foundation of Lüliang Airport[J]. Chinese Journal of Geotechnical Engineering, 2013, 35(2): 293-301. (in Chinese with English abstract)

    [3] 姚仰平,黄建,张奎,等. 机场高填方蠕变沉降的数值反演预测[J]. 岩土力学,2020,41(10):3395 − 3404. [YAO Yangping,HUANG Jian,ZHANG Kui,et al. Numerical back-analysis of creep settlement of airport high fill[J]. Rock and Soil Mechanics,2020,41(10):3395 − 3404. (in Chinese with English abstract) DOI: 10.16285/j.rsm.2020.0402

    YAO Yangping, HUANG Jian, ZHANG Kui, et al. Numerical back-analysis of creep settlement of airport high fill[J]. Rock and Soil Mechanics, 2020, 41(10): 3395-3404. (in Chinese with English abstract) DOI: 10.16285/j.rsm.2020.0402

    [4] 宰金珉,梅国雄. 全过程的沉降量预测方法研究[J]. 岩土力学,2000,21(4):322 − 325. [ZAI Jinmin,MEI Guoxiong. Forecast method of settlement during the complete process of construction and operation[J]. Rock and Soil Mechanics,2000,21(4):322 − 325. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-7598.2000.04.003

    ZAI Jinmin, MEI Guoxiong. Forecast method of settlement during the complete process of construction and operation[J]. Rock and Soil Mechanics, 2000, 21(4): 322-325. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-7598.2000.04.003

    [5] 刘射洪, 袁聚云, 赵昕. 地基沉降预测模型研究综述[J]. 工业建筑, 2014, 44(增刊1): 738 − 741

    LIU Shehong, YUAN Juyun, ZHAO Xin. Review of settlement prediction models of foundation[J]. Industrial Construction, 2014, 44(Sup 1): 738 − 741. (in Chinese with English abstract)

    [6] 周艳萍. 基于灰色Verhulst模型的山西太原地面沉降趋势分析[J]. 中国地质灾害与防治学报,2018,29(2):94 − 99. [ZHOU Yanping. Land subsidence trend of Taiyuan City,Shanxi based on Grey Verhust Model[J]. The Chinese Journal of Geological Hazard and Control,2018,29(2):94 − 99. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2018.02.15

    ZHOU Yanping. Land subsidence trend of Taiyuan City, Shanxi based on Grey Verhust Model[J]. The Chinese Journal of Geological Hazard and Control, 2018, 29(2): 94-99. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2018.02.15

    [7] 范珊珊,郭海朋,朱菊艳,等. 线性回归模型在北京平原地面沉降预测中的应用[J]. 中国地质灾害与防治学报,2013,24(1):70 − 74. [FAN Shanshan,GUO Haipeng,ZHU Juyan,et al. Application of linear regression model for land subsidence prediction in Beijing plain[J]. The Chinese Journal of Geological Hazard and Control,2013,24(1):70 − 74. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2013.01.016

    FAN Shanshan, GUO Haipeng, ZHU Juyan, et al. Application of linear regression model for land subsidence prediction in Beijing plain[J]. The Chinese Journal of Geological Hazard and Control, 2013, 24(1): 70-74. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2013.01.016

    [8] 韩相超,吕远强. 内蒙古黄旗海湿地软土路基的沉降规律分析[J]. 中国地质灾害与防治学报,2013,24(1):75 − 78. [HAN Xiangchao,LYU Yuanqiang. Settlement analysis of soft subgrade in Huangqihai wetlands,Inner Mongolia[J]. The Chinese Journal of Geological Hazard and Control,2013,24(1):75 − 78. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2013.01.005

    HAN Xiangchao, LV Yuanqiang. Settlement analysis of soft subgrade in Huangqihai wetlands, Inner Mongolia[J]. The Chinese Journal of Geological Hazard and Control, 2013, 24(1): 75-78. (in Chinese with English abstract) DOI: 10.16031/j.cnki.issn.1003-8035.2013.01.005

    [9] 葛苗苗,李宁,郑建国,等. 基于一维固结试验的压实黄土蠕变模型[J]. 岩土力学,2015,36(11):3164 − 3170. [GE Miaomiao,LI Ning,ZHENG Jianguo,et al. A creep model for compacted loess based on 1D oedometer test[J]. Rock and Soil Mechanics,2015,36(11):3164 − 3170. (in Chinese with English abstract) DOI: 10.16285/j.rsm.2015.11.017

    GE Miaomiao, LI Ning, ZHENG Jianguo, et al. A creep model for compacted loess based on 1D oedometer test[J]. Rock and Soil Mechanics, 2015, 36(11): 3164-3170. (in Chinese with English abstract) DOI: 10.16285/j.rsm.2015.11.017

    [10] 宰金珉,梅国雄. 泊松曲线的特征及其在沉降预测中的应用[J]. 重庆建筑大学学报,2001,23(1):30 − 35. [ZAI Jinmin,MEI Guoxiong. Feature of poisson curve and its application to displacement forecast[J]. Journal of Chongqing Jianzhu University,2001,23(1):30 − 35. (in Chinese with English abstract)

    ZAI Jinmin, MEI Guoxiong. Feature of Poisson curve and its application to displacement forecast[J]. Journal of Chongqing Jianzhu University, 2001, 23(1): 30-35. (in Chinese with English abstract)

    [11] 曹文贵, 印鹏, 贺敏, 等. 考虑实测数据新旧程度的恭候澄江单项模型预测方法[J]. 水文地质工程地质, 2015, 42(6): 65 − 70

    CAO Wengui, YIN Peng, HE Min, et al. A prediction method for post-construction settlement ofa single model with the consideration of the new or old degree of the measured data[J]. Hydrogeology & Engineering Geology, 2015, 42(6): 65 − 70. (in Chinese with English abstract)

    [12]

    VAGHI C,RODALLEC A,FANCIULLINO R,et al. Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors[J]. PLoS Computational Biology,2020,16(2):e1007178. DOI: 10.1371/journal.pcbi.1007178

    [13] 余闯,刘松玉. 路堤沉降预测的Gompertz模型应用研究[J]. 岩土力学,2005,26(1):82 − 86. [YU Chuang,LIU Songyu. A Study on prediction of embankment settlement with the gompertz model[J]. Rock and Soil Mechanics,2005,26(1):82 − 86. (in Chinese with English abstract)

    YU Chuang, LIU Songyu. A Study on prediction of embankment settlement with the gompertz model[J]. Rock and Soil Mechanics, 2005, 26(1): 82-86. (in Chinese with English abstract)

    [14] 胡顺强,崔东文. 基于AEO-Schumacher-Usher模型的径流及地下水位预测[J]. 中国农村水利水电,2020(11):28 − 34. [HU Shunqiang,CUI Dongwen. Runoff and groundwater level prediction based on AEO-schumacher-usher model[J]. China Rural Water and Hydropower,2020(11):28 − 34. (in Chinese with English abstract) DOI: 10.3969/j.issn.1007-2284.2020.11.006

    HU Shunqiang, CUI Dongwen. Runoff and groundwater level prediction based on AEO-schumacher-usher model[J]. China Rural Water and Hydropower, 2020(11): 28-34. (in Chinese with English abstract) DOI: 10.3969/j.issn.1007-2284.2020.11.006

    [15]

    DE OLIVEIRA FERREIRA D J,DE MATTOS FIUZA M P,CARDOSO M,et al. Use of the Weibull model on sizing thickeners-Part I:Sedimentation curve representation[J]. The Canadian Journal of Chemical Engineering,2021,99(3):708 − 724. DOI: 10.1002/cjce.23904

    [16] 刘国辉. Weibull模型在地基沉降预测中的应用[J]. 贵州大学学报(自然科学版),2011,28(2):111 − 114. [LIU Guohui. The application of weibull model to settlement prediction of foundation[J]. Journal of Guizhou University (Natural Sciences),2011,28(2):111 − 114. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-5269.2011.02.029

    LIU Guohui. The application of weibull model to settlement prediction of foundation[J]. Journal of Guizhou University (Natural Sciences), 2011, 28(2): 111-114. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-5269.2011.02.029

    [17] 王军保,刘新荣,李鹏,等. MMF模型在采空区地表沉降预测中的应用[J]. 煤炭学报,2012,37(3):411 − 415. [WANG Junbao,LIU Xinrong,LI Peng,et al. Study on prediction of surface subsidence in mined-out region with the MMF model[J]. Journal of China Coal Society,2012,37(3):411 − 415. (in Chinese with English abstract) DOI: 10.13225/j.cnki.jccs.2012.03.024

    WANG Junbao, LIU Xinrong, LI Peng, et al. Study on prediction of surface subsidence in mined-out region with the MMF model[J]. Journal of China Coal Society, 2012, 37(3): 411-415. (in Chinese with English abstract) DOI: 10.13225/j.cnki.jccs.2012.03.024

    [18]

    ZREIQ R,KAMEL S,BOUBAKER S,et al. Generalized Richards model for predicting COVID-19 dynamics in Saudi Arabia based on particle swarm optimization Algorithm[J]. AIMS Public Health,2020,7(4):828 − 843. DOI: 10.3934/publichealth.2020064

    [19]

    HUANG Changfu,LI Qun,WU Shunchuan,et al. Application of the richards model for settlement prediction based on a bidirectional difference-weighted least-squares method[J]. Arabian Journal for Science and Engineering,2018,43(10):5057 − 5065. DOI: 10.1007/s13369-017-2909-0

    [20]

    HU Qingfeng,CUI Ximin,WANG Guo,et al. Key technology of predicting dynamic surface subsidence based on knothe time function[J]. Journal of Software,2011,6(7):1273 − 1280.

    [21]

    CHEN Lei,ZHANG Liguo,TANG Yixian,et al. Analysis of mining-induced subsidence prediction by exponent knothe model combined with insar and leveling[J]. ISPRS Annals of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2018,IV-3:53 − 59.

    [22] 高超,徐乃忠,孙万明,等. 基于Bertalanffy时间函数的地表动态沉陷预测模型[J]. 煤炭学报,2020,45(8):2740 − 2748. [GAO Chao,XU Naizhong,SUN Wanming,et al. Dynamic surface subsidence prediction model based on Bertalanffy time function[J]. Journal of China Coal Society,2020,45(8):2740 − 2748. (in Chinese with English abstract)

    GAO Chao, XU Naizhong, SUN Wanming, et al. Dynamic surface subsidence prediction model based on Bertalanffy time function[J]. Journal of China Coal Society, 2020, 45(8): 2740-2748. (in Chinese with English abstract)

    [23]

    LEE L,ATKINSON D,HIRST A G,et al. A new framework for growth curve fitting based on the von Bertalanffy Growth Function[J]. Scientific Reports,2020,10(1):1 − 12. DOI: 10.1038/s41598-019-56847-4

    [24] 邓英尔,谢和平. 全过程沉降预测的新模型与方法[J]. 岩土力学,2005,26(1):1 − 4. [DENG Yinger,XIE Heping. New model and method of forecasting settlement during complete process of construction and operation[J]. Rock and Soil Mechanics,2005,26(1):1 − 4. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-7598.2005.01.001

    DENG Yinger, XIE Heping. New model and method of forecasting settlement during complete process of construction and operation[J]. Rock and Soil Mechanics, 2005, 26(1): 1-4. (in Chinese with English abstract) DOI: 10.3969/j.issn.1000-7598.2005.01.001

    [25] 王志亮,吴克海,李永池,等. 一个预测路堤沉降的新经验公式模型[J]. 岩石力学与工程学报,2005,24(12):2013 − 2017. [WANG Zhiliang,WU Kehai,LI Yongchi,et al. A new empirical formula model for settlement prediction of embankments[J]. Chinese Journal of Rock Mechanics and Engineering,2005,24(12):2013 − 2017. (in Chinese with English abstract) DOI: 10.3321/j.issn:1000-6915.2005.12.003

    WANG Zhiliang, WU Kehai, LI Yongchi, et al. A new empirical formula model for settlement prediction of embankments[J]. Chinese Journal of Rock Mechanics and Engineering, 2005, 24(12): 2013-2017. (in Chinese with English abstract) DOI: 10.3321/j.issn:1000-6915.2005.12.003

    [26]

    REDDY B R,OJHA A. Performance of maintainabilityiindex prediction models:A feature selection based study[J]. Evolving Systems,2019,10(2):179 − 204. DOI: 10.1007/s12530-017-9201-0

    [27] 方薇,陈向阳.考虑次固结的软基分级加载全过程沉降模型[J].中国地质灾害与防治学报, 2015, 26(2): 110-115

    FANG Wei, CHEN Xiangyang.Foundation settlement model considering secondary consolidation during multi-stage loading[J]. The Chinese Journal of Geological Hazard andControl, 2015, 26(2): 110-115.(in Chinese with English abstract)

    [28] 韩相超, 吕远强.内蒙古黄旗海湿地软土路基的沉降规律分析[J].中国地质灾害与防治学报, 2013, 24(1): 75-78.

    HAN Xiangchao, LYU Yuanqiang. Settlement analysis of soft subgrade in Huangqihai wetlands, Inner Mongolia[J]. The Chinese Journal of Geological Hazard and Control, 2013, 24(1): 75-78.(in Chinese with English abstract)

    [29]

    LI Shouju,YU Shen,SHANGGUAN Zichang,et al. Estimating model parameters of rockfill materials based on genetic algorithm and strain measurements[J]. Geomechanics and Engineering,2016,10(1):37 − 48. DOI: 10.12989/gae.2016.10.1.037

    [30] 甘友文,王志亮, 郑华.地基沉降预测中的双曲线模型修正[J].水文地质工程地质, 2004, 31(1): 98-100.

    GAN Youwen, WANG Zhiliang, ZHENG Hua. Modification of hyperbolic model in foundation settlement prediction[J]. Hydrogeology & Engineering Geology, 2004, 31(1): 98-100.(in Chinese)

    [31] 刘宏,李攀峰,张倬元. 九寨黄龙机场高填方地基工后沉降预测[J]. 岩土工程学报,2005,27(1):90 − 93. [LIU Hong,LI Panfeng,ZHANG Zhuoyuan. Prediction of the post-construction settlement of the high embankment of Jiuzhai-Huanglong Airport[J]. Chinese Journal of Geotechnical Engineering,2005,27(1):90 − 93. (in Chinese with English abstract) DOI: 10.3321/j.issn:1000-4548.2005.01.015

    LIU Hong, LI Panfeng, ZHANG Zhuoyuan. Prediction of the post-construction settlement of the high embankment of Jiuzhai-Huanglong Airport[J]. Chinese Journal of Geotechnical Engineering, 2005, 27(1): 90-93. (in Chinese with English abstract) DOI: 10.3321/j.issn:1000-4548.2005.01.015

    [32] 王海英,常肖,阮祺,等. 建筑垃圾填埋路基沉降预测的三点-星野法[J]. 铁道科学与工程学报,2017,14(3):473 − 479. [WANG Haiying,CHANG Xiao,RUAN Qi,et al. Subsidence prediction of subgrade filled by construction waste based on three point-hoshino algorithm[J]. Journal of Railway Science and Engineering,2017,14(3):473 − 479. (in Chinese with English abstract) DOI: 10.3969/j.issn.1672-7029.2017.03.006

    WANG Haiying, CHANG Xiao, RUAN Qi, et al. Subsidence prediction of subgrade filled by construction waste based on three point-hoshino algorithm[J]. Journal of Railway Science and Engineering, 2017, 14(3): 473-479. (in Chinese with English abstract) DOI: 10.3969/j.issn.1672-7029.2017.03.006

    [33] 中华人民共和国住房和城乡建设部. 建筑变形测量规范: JGJ 8—2016[S]. 北京: 中国建筑工业出版社, 2016

    Ministry of Housing and Urban-Rural Development of the People's Republic of China. Code for deformation measurement of building and structure: JGJ 8—2016[S]. Beijing: China Architecture & Building Press, 2016. (in Chinese)

    [34]

    LEWIS C D. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting[M]. London: Butterworth Scientific, 1982.

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  • 收稿日期:  2022-11-01
  • 修回日期:  2023-03-14
  • 录用日期:  2023-03-29
  • 网络出版日期:  2023-04-03
  • 刊出日期:  2023-08-21

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