Settlement Prediction of Dredger Fill with the Optimal Combination Model

Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. e., cannot reflect various regulations of settlement at some stages or the entire process). In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.

A new approach for time effect analysis of settlement for single pile based on virtual soil-pile model

A new approach is proposed to analyze the settlement behavior for single pile embedded in layered soils. Firstly, soil layers surrounding pile shaft are simulated by using distributed Voigt model, and finite soil layers under the pile end are assumed to be virtual soil-pile whose cross-section area is the same as that of the pile shaft. Then, by means of Laplace transform and impedance function transfer method to solve the static equilibrium equation of pile, the analytical solution of the displacement impedance fimction at the pile head is derived. Furthermore, the analytical solution of the settlement at the head of single pile is theoretically derived by virtue of convolution theorem. Based on these solutions, the influences of parameters of soil-pile system on the settlement behavior for single pile are analyzed. Also, comparison of the load-settlement response for two well-instrumented field tests in multilayered soils is given to demonstrate the effectiveness and accuracy of the proposed approach. It can be noted that the presented solution can be used to calculate the settlement of single pile for the preliminary design of pile foundation.

Settlement prediction model of slurry suspension based on sedimentation rate attenuation

This paper introduces a slurry suspension settlement prediction model for cohesive sediment in a still water environment. With no sediment input and a still water environment condition, control forces between settling particles are significantly different in the process of sedimentation rate attenuation, and the settlement process includes the free sedimentation stage, the log-linear attenuation stage, and the stable consolidation stage according to sedimentation rate attenuation. Settlement equations for sedimentation height and time were established based on sedimentation rate attenuation properties of different sedimentation stages. Finally, a slurry suspension settlement prediction model based on slurry parameters was set up with a foundation being that the model parameters were determined by the basic parameters of slurry. The results of the settlement prediction model show good agreement with those of the settlement column experiment and reflect the main characteristics of cohesive sediment. The model can be applied to the prediction of cohesive soil settlement in still water environments.

Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

Deformation prediction model of concrete face rockfill dams based on an improved random forest model

The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.

Settlement Prediction for Buildings Surrounding Foundation Pits Based on a Stationary Auto-regression Model

To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.

Life Prediction Model for a Nickel-base Single Crystal Superalloy DD3

The possibility of a life prediction model for nickel base single crystal blades has been studied. The fatigue creep (FC) and thermal fatigue creep(TMFC) as well as creep experiments have been carried out with different hold time of DD3. The hold time and the frequency as well as the temperature range are the main factors influencing the life. An emphasis has been put on the micro mechanism of the rupture of creep, FC and TMFC. Two main factors are the voiding and degeneration of the material for the cre...

Early Warning of Foundation Settlement Deformation for Ballastless High-Speed Railway Tracks

There has been rapid development of high-speed railway lines, especially passenger-dedicated railway lines, in China. Trains are traveling at speeds exceeding 250 km per hour and they require highly smooth tracks to ensure safety. However, there have been no in-depth studies on the early warning of the settlement of high-speed railway lines in China or abroad. Most methods use a simple model based on data processing and decision rules. The core issues of early warning lie in the science and rationality of decision rules. The present paper therefore investigates novel and critical indexes for the warning of settlement under high-speed railway lines according to existing norms and field data, and several essential indexes of deformation warning are suggested through theoretical and experimental analysis.

Dynamic Comparison and Trend analysis of Southeast Guizhou's Experimental Area of Eco-civilization Construction and the National Eco-efficiency

In order to better understand the development level of eco-efficiency in Southeast Guizhou's experimental area of eco-civilization construction,we conduct dynamic comparison research of its eco-efficiency and the national eco-efficiency,using single ratio method based on the ecological footprint model,to grasp the gap between its eco-efficiency and the national eco-efficiency,so that we can take appropriate countermeasures to improve eco-efficiency. The results show that in the period 1978-2010,the eco-efficiency in Southeast Guizhou's experimental area of eco-civilization construction was always lower than the national eco-efficiency; the long-time average annual value of its eco-efficiency was less than one half of that of the national eco-efficiency,with the absolute gap of 1 630. 095 yuan /hm 2 ,and the gap tended to widen year by year in the period 1978-2002 ( the gap increased from 276. 551 yuan /hm 2 in 1978 to peak of 3 227. 713 yuan /hm 2 in 2002,with an average annual increase of 118. 047 yuan /hm 2 ,and especially after 1992,the gap was particularly evident,with an average annual increase of 194.771 yuan/hm 2 ) ,but from 2003,the gap between the two tended to decrease. Based on the prediction results of grey system,in the period 2011-2025,the gap between the eco-efficiency in Southeast Guizhou's experimental area of eco-civilization construction and the national eco-efficiency will gradually narrow,and from 2019, the eco-efficiency in Southeast Guizhou's experimental area of eco-civilization construction will be higher than the national eco-efficiency.

自发性气胸单孔胸腔镜手术后肺部并发症预测模型的构建

目的:探讨自发性气胸单孔胸腔镜手术后肺部并发症预测模型的构建。方法:选取2021年5月至2022年5月行单孔胸腔镜手术的106例自发性气胸患者,根据术后是否发生肺部并发症将患者分为并发症组(n=22)与无并发症组(n=84)。对两组患者临床资料进行单因素与多因素分析,筛选出影响术后肺部并发症的危险因素,并构建列线图预测模型。结果:并发症组患者年龄、吸烟史、合并肺部疾病、胸膜粘连严重、肺大疱数量、术后24 h疼痛分级、术中出血量高于无并发症组,术前白蛋白水平低于无并发症组。多因素Logistic回归分析结果显示,年龄≥50岁(OR=1.240,95%CI=1.064~1.445)、合并肺部疾病(OR=26.154,95%CI=1.224~558.648)、胸膜粘连严重(OR=23.289,95%CI=2.940~184.520)、肺大疱数量>2个(OR=2.878,95%CI=1.787~4.633)是影响术后肺部并发症的独立危险因素(P<0.05)。根据以上危险因素建立预测模型预测术后肺部并发症的受试者工作特征曲线下面积为0.82(95%CI=0.756~0.874),敏感度与特异度分别为82.33%与65.47%。结论:年龄、合并肺部疾病、胸膜粘连严重、肺大疱数量是影响自发性气胸单孔胸腔镜手术后肺部并发症的独立危险因素,根据其构建的列线图预测模型对术后肺部并发症的发生风险具有较好的预测价值。

Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller

In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.

囊袋式注浆对盾构下穿高速铁路路基沉降的控制效果

高铁路基的沉降变形是影响运营安全的重要因素,盾构下穿既有高铁线路施工可诱发高铁路基沉降变形。为有效控制高铁路基沉降,基于数值模拟探究了囊袋式注浆作为高铁路基沉降主动控制措施的有效性。结果表明:注浆囊袋对其周围土体的影响具有局部性,地层等效塑性应变随注浆体埋深的增加而增大,且塑性区厚度通常小于注浆囊袋膨胀厚度的3.0倍。囊袋的埋设深度和膨胀直径对路基抬升整治范围有影响,囊袋埋设越浅其膨胀后对路基抬升的影响效果越明显,且路基表层的隆起变形曲线符合高斯方程。提出了盾构下穿高铁路基时囊袋式注浆控制措施下路基沉降预测模型,可指导盾构下穿高铁路基的沉降控制。

西南某机场跑道沉降预测模型

机场道面沉降,严重影响机场安全运行。准确预测跑道工后沉降,对机场的建设与运行极为重要。以西南某机场跑道沉降变形的观测数据为依据,分别用双曲线模型、对数模型、指数模型以及灰色预测模型,对跑道沉降进行预测和对比分析,解决了小样本数下曲线预测精度较低及灰色模型对非线性预测准确度差等问题,提高了预测的精度;同时通过BP神经网络对组合预测模型的残差进行修正,最大限度地提高模型预测的精度和效果,为地基沉降预测提供借鉴。

有源缓冲型逆变器功率解耦技术研究

为实现单级单相升压逆变并能有效抑制直流侧二次纹波电流,提出一种带有源缓冲的电流型逆变器电路拓扑,研究该逆变器的6种电路模态并提出其两模态调制策略和三模态调制策略,设计2种调制策略下的模型预测控制策略,提供主电路参数设计准则。从储能电感电流的最大脉动量与二次纹波含量两方面对2种调制策略进行对比分析,得出了三模态调制策略具有更好纹波抑制效果的结论。对2种调制策略下的逆变器进行仿真分析,仿真结果表明,三模态调制下电路的输出电压总谐波失真率、电感电流相对脉动量与二倍频含量均低于两模态调制下的数据。设计并搭建500VA 48VDC/220V50HzAC有源缓冲型单级单相升压逆变器实验装置,给出了三模态调制下基于模型预测控制的逆变器实验波形,进一步证实了所提研究方案在实现单级升压逆变的同时具有二次纹波抑制能力。

台阶单孔爆破振动预测模型研究

爆破振动具有随机性,振动波的强度和频率具有非平稳性,为了构建能够真实反映爆破振动波形特性的台阶单孔爆破振动预测模型,通过调制白噪声的过程确定模型的频率函数,用角频率的时变性来描述振动波形频率的非平稳性;通过对强度包络函数的筛选,选取Gamma函数来描述爆破振动波形强度的非平稳性;用高通滤波处理提高了预测模型在低频段的模拟精度.以预测模型与现场实测波形的时程频率一致为约束条件,阻尼比与不规则指数呈线性关系,确定模型的频率参数(η,ξ,γ),以现场实测波形累计能量为约束条件,确定模型的强度参数(I0,α,β),模型输出波形的峰值振速、主频分别与现场实测波形特征的较好吻合,证明了所构建预测模型的正确性和有效性.

基于BP神经网络的乒乓球优秀女子单打比赛结果预测模型构建及仿真应用

为构建优秀女子运动员技战术表现与比赛获胜的理论模型,合理安排技战术训练及比赛策略提供参考借鉴。运用录像观察法、数理统计法及BP神经网络构建乒乓球优秀女子单打比赛结果预测模型。在此基础上,利用预测模型仿真分析新型塑料球时代优秀女子乒乓球运动员在不同水平技战术组合下的比赛获胜模式。本文构建的预测模型R为0.978,R~2为0.956,平均绝对误差为0.0085,模型精度达到98.4%;仿真分析1 024种技战术段组合结果可知,568种组合的预测结果为获胜,456种组合的预测结果为失败。结论:基于BP神经网络构建的乒乓球优秀女子单打比赛结果预测模型拟合效果佳,个案实证预测效果较好,具有较高的预测性能;新型塑料球时代优秀女子运动员在单打比赛中,各技战术段之间的补偿效应因技战术段和等级而有所不同,评估总分17分为女子单打比赛胜负的分界点,不同水平技战术段组合的比赛评估总分大于17分即可取得比赛胜利,低于17分则会落败。

基于优化长短期记忆网络的矿坑遗产沉降预测

工业矿坑遗产以其独特风貌和价值逐步受到广泛关注。针对矿坑遗产易发的沉降地质灾害,积极采取预防措施是降低损失的有效途径。为解决工业矿坑遗产沉降灾害预测问题,提出一种融合蜣螂优化算法(DBO)的优化长短期记忆网络(LSTM)算法,用于构建预警模型。选取阜新市海州露天矿作为实验地点,利用小基线集合成孔径雷达干涉测量(SBAS-InSAR)技术采集55景矿区沉降数据。通过两种去噪方法对采集到的样本数据进行去噪处理,应用DBO算法优化LSTM,建立工业矿坑遗产沉降预测模型。LSTM模型的超参数使用DBO算法优化以实现高精度预测模型,并与其他算法优化LSTM后的模型指标进行对比。结果表明:DBO-LSTM模型在工业矿坑遗产沉降预测优势突出,预测模型的均方根误差、平均绝对误差和决定系数分别为0.045 mm,0.038 mm,0.956,均优于其他预测模型。DBO-LSTM模型在预测工业矿坑遗产沉降方面展现了高精度、快速收敛和强稳定性等特点,为工业矿坑遗产保护工作提供了有力支持。

宁波软土地区地铁联络通道冻结法工后长期运营沉降预测研究

宁波地区主要以海相淤泥质黏土或淤泥等软土为主,此类土具有含水量大、压缩性高、承载力低的特性,地铁围岩开挖施工面临垮塌、变形问题,冻结法施工往往是处理上述问题的有效方法。然而,地层解冻后注浆作业不及时、注浆效果差极易导致地层沉降,所以需要对地基沉降进行长期监测。为更好考虑软土地基下的隧道安全,以宁波句章路站-鄞州客运总站站-南部商务区站区间盾构隧道为例,采用冻结法对宁波句章路站-鄞州客运总站站区间1#联络通道进行施工,开展冻融工后联络通道长期运营沉降预测研究,并通过三维有限软件MIDAS/GTS建模,对其冻融沉降进行计算分析。根据布点及实时观测,对隧道及句鄞区间盾构隧道及1#联络通道开挖施工期间的结构沉降变形及长期运营沉降变形进行预测,预测结果与监测数据基本一致,对于软土地基冻融处理具有重要工程意义,同时也可为相关工程提供借鉴。

基于TCN与SVM组合模型的建筑沉降预测

针对基坑周边建筑沉降序列随时间和因其他影响因素呈现的非平稳性变化特征,提出一种基于TCN与SVM的短期沉降组合预测方法。该方法利用串联组合的方式,将沉降序列看作自相关的时间序列和受外界影响的非线性部分的组合,再利用TCN与SVM模型对两部分分别进行预测,最后将两部分叠加后得到沉降数据预测结果。结果表明,TCN-SVM组合预测模型能够很好地跟踪建筑沉降变化,其预测精度比单一SVM模型提高10%~20%,可有效提高短期建筑沉降预测精度。

基于模型预测控制的输入并联输出串联双有源桥变换器均压控制策略

以双有源桥变换器(dual-active-bridge converters, DAB)为子模块、输入端并联输出端串联(input parallel output series, IPOS)构成的IPOS-DAB变换器是全直流风电系统实现风电直流汇集与送出的关键枢纽。IPOS-DAB变换器在工程实际应用中元器件参数存在差异,并串联时不同DAB子模块输出功率不均衡,导致DAB子模块输出电压电流不均衡,进而会造成子模块损坏的机率大增,极大地降低了系统的可靠性。传统均压控制存在可靠性较低、动态性能不足的问题,因此无法满足外部动态扰动时的控制要求。针对此问题,结合模型预测控制(model predictive control, MPC)方法的优势,采用模型预测控制对IPOS-DAB变换器进行均压控制。首先根据IPOS-DAB变换器建立预测模型,然后设计控制目标函数及整体控制策略。最后在Matlab/Simulink中搭建系统模型,在不同工况下与传统均压控制进行对比仿真验证,证明了所提控制策略具有更好的均压控制效果。