基于CPTu数据的黏性土不排水抗剪强度#br# 神经网络预测

利用人工神经网络模型,建立基于孔压静力触探（CPTu）现场测试数据的黏性土不排水抗剪强度的预测方法。为建立和验证人工神经网络模型,在3个场地开展CPTu和十字板剪切现场测试,共取得33个测孔的CPTu试验数据和相对应的不排水抗剪强度实测值。通过对比分析不同输入向量、不同网络隐层数、不同神经元数及不同改进算法对人工神经网络模型性能的影响,确定人工神经网络模型的具体形式。通过对训练组数据开展机器学习,所建立的人工神经网络模型能够有效地基于CPTu获得的端阻力和孔隙水压力现场测试数据对黏土不排水抗剪强度进行预测,预测结果与十字板剪切试验实测结果非常接近。与传统用于估算不排水强度的经验关系相比,采用人工神经网络模型预测结果与实测结果相关性显著提高、误差明显降低。     

Analysis of ground surface settlement in anisotropic clays using extreme gradient boosting and random forest regression models

Excessive ground surface settlement induced by pit excavation (i.e. braced excavation) can potentially result in damage to the nearby buildings and facilities. In this paper, extensive finite element analyses have been carried out to evaluate the effects of various structural, soil and geometric properties on the maximum ground surface settlement induced by braced excavation in anisotropic clays. The anisotropic soil properties considered include the plane strain shear strength ratio (i.e. the ratio of the passive undrained shear strength to the active one) and the unloading shear modulus ratio. Other parameters considered include the support system stiffness, the excavation width to excavation depth ratio, and the wall penetration depth to excavation depth ratio. Subsequently, the maximum ground surface settlement of a total of 1479 hypothetical cases were analyzed by various machine learning algorithms including the ensemble learning methods (extreme gradient boosting (XGBoost) and random forest regression (RFR) algorithms). The prediction models developed by the XGBoost and RFR are compared with that of two conventional regression methods, and the predictive accuracy of these models are assessed. This study aims to highlight the technical feasibility and applicability of advanced ensemble learning methods in geotechnical engineering practice.     

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R² value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.     

Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential

This study investigates the performance of four machine learning (ML) algorithms to evaluate the earthquake-induced liquefaction potential of soil based on the cone penetration test field case history records using the Bayesian belief network (BBN) learning software Netica. The BBN structures that were developed by ML algorithms-K2, hill climbing (HC), tree augmented naive (TAN) Bayes, and Tabu search were adopted to perform parameter learning in Netica, thereby fixing the BBN models. The performance measure indexes, namely, overall accuracy (OA), precision, recall, F-measure, and area under the receiver operating characteristic curve, were used to evaluate the training and testing BBN models’ performance and highlight the capability of the K2 and TAN Bayes models over the Tabu search and HC models. The sensitivity analysis results showed that the cone tip resistance and vertical effective stress are the most sensitive factors, whereas the mean grain size is the least sensitive factor in the prediction of seismic soil liquefaction potential. The results of this study can provide theoretical support for researchers in selecting appropriate ML algorithms and improving the predictive performance of seismic soil liquefaction potential models.     

Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand. The primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams (SB). A database encompassing 1118 experimental findings from the relevant literature was compiled, containing eight distinct factors. Gradient Boosting (GB) technique was developed and evaluated in combination with three different optimization algorithms, namely Particle Swarm Optimization (PSO), Random Annealing Optimization (RA), and Simulated Annealing Optimization (SA). The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output variables. Shap values and two-dimensional PDP analysis were then carried out. Engineers may use the findings in this work to define beam's geometrical components and material used to achieve the desired shear strength of SB without reinforcement.     

Hybrid random aggregation model and Bayesian optimization-based convolutional neural network for estimating the concrete compressive strength

Numerous experimental studies have shown the type and gradation of coarse aggregates effect on the mechanical properties of concrete. The type and gradation of coarse aggregates have not been taken into account in the available machine learning prediction models. In this study, a two-dimensional concrete microscopic image was generated by using a random aggregate model (RAM), and the coarse aggregate and other concrete ingredients were represented innovatively using polygons and trichromatic chromaticity values in the RAM images. The RAM image set was created by applying this method to represent 1110 sets of different concrete mixes. Then based on the Bayesian optimization algorithm and the image set, a compressive strength prediction model considering the effect of coarse aggregate types and gradations was developed utilizing a convolutional neural network (CNN) model. Meanwhile, an artificial neural network (ANN) compressive strength prediction model was developed using 1110 sets of mix ratio data. The results show that the proposed RAM image generation method has the capability to represent different concrete mix ratios collected in this study. The prediction performance of the CNN compressive strength model considering aggregate types and gradations is better than that of the ANN model. The method can provide a new perspective for predicting other concrete mechanical properties and technically support performance-based intelligent concrete mix design.     

Predicting construction crew productivity by using Self Organizing Maps

A Self Organizing Map (SOM), is a machine learning method that represents high-dimensional data in low-dimensional form without losing topological relations of the data. After an unsupervised learning process, it organizes the data on the basis on similarity. In the current study, a SOM based algorithm has been developed which not only produces 2-D maps to analyze the relationship between various factors and crew productivity, but also predicts productivity under given conditions. Validation of the model has been achieved both by using artificial data set and data from 144 concrete pouring, 101 formwork and 101 reinforcement crews. The results show that maps which are produced by the model are satisfactory in clustering the data and prediction performance of the model is superior to similar artificial neural network models.     

Data-driven vision-based inspection for reinforced concrete beams and slabs: Quantitative damage and load estimation

We show that computer-vision-based inspection can relate surface observations to quantitative damage and load level estimates in common reinforced concrete beams and slabs subjected to monotonic loading. This work is related to an earlier study focused on shear-critical beams and slabs (i.e., specimens lacking shear reinforcement), but here an expanded image database has been assembled to include specimens with both flexural and shear reinforcement such as would be found in practice. Using this expanded data set, a supervised machine learning algorithm builds cross-validated predictive models capable of estimating internal loads (i.e., shear and moment) and damage levels based on surface crack pattern images. The expanded data set contains a total of 127 specimens and 862 images captured in past studies across a range of load and damage levels. Textural and geometric attributes of surface crack patterns were used for feature engineering and tuning of predictive models. The expanded data set enables comparison of the estimation accuracy for shear-critical and shear-reinforced beams and slabs considered separately and in combined form. This includes the capability to categorize whether shear reinforcement is present or not. Estimation models based on surface observations for shear-reinforced elements are found to be comparable to those for shear-critical beams and slabs, with variability observed due to loading type range, member geometries, whether categorization is combined with regression, and the image feature sets used.     

基于CPTU数据融合技术的黏性土应力历史与强度特性评价研究

超固结比(OCR)和不排水抗剪强度(S_u)是土的基本力学参数,对土体沉降变形分析和稳定性计算具有重要影响。采用数据融合技术,结合孔压静力触探(CPTU)测试数据,提出了江苏典型黏性土超固结比和不排水抗剪强度的预测模型。利用特征级数据融合技术(回归树、模型树)与决策级数据融合技术(自举聚合、堆叠泛化)对预测模型的可行性进行分析。将土的超固结比和不排水抗剪强度的预测值、室内试验所得到的参考值以及CPTU传统方法所得到的估计值进行对比分析。结果表明,模型树预测结果比回归树要好,决策级融合算法可以提高回归树的预测结果,但对模型树的预测结果影响较小;叠加回归树和模型树的预测结果会使其预测的不排水抗剪强度比回归树预测的结果要好,但比模型树预测的结果要差;对于几种数据融合模型,OCR预测值大致相当,回归树模型在预测OCR方面稍优于其他数据融合模型,数据融合技术能更好地预测土的超固结比和不排水抗剪强度。     

基于循环神经网络的盾构隧道#br# 引发地面最大沉降预测

伴随着计算机技术的快速发展,机器学习等新兴算法正在被越来越多地运用于预测隧道掘进引发的地面最大沉降。在隧道施工过程中,由盾构机和地面监测点位采集的数据具有很强的序列化特征,而传统的机器学习算法对序列数据的处理存在一定的局限性。循环神经网络(RNN)具有极强的对时序型数据的处理能力,在视频识别、语音翻译等领域有着广泛的应用。采用两种RNN模型(LSTM、GRU)和传统的BP神经网络模型,以地质参数、几何参数和盾构机参数作为输入,对隧道施工过程中引发的地面最大沉降进行预测分析。结果显示,RNN对隧道沉降的预测结果优于传统的BP神经网络模型,并且RNN在连续未知区段的预测结果比BPNN更加稳定。     

A knowledge transfer enhanced ensemble approach to predict the shear capacity of reinforced concrete deep beams without stirrups

This paper proposes a novel learning algorithm, the transfer ensemble neural network (TENN) model, to increase the performance of shear capacity predictions on small datasets, illuminating the usefulness of advanced machine learning techniques in general. By incorporating ensemble learning and transfer learning, the TENN model is designed to control the high variability inherent in machine learning models trained on small amounts of data. The novel TENN model is validated to predict the shear capacity of deep reinforced concrete (RC) beams without stirrups across varying data availability levels. Knowledge acquired through pretraining a model on slender RC beams is utilized for training a model to better predict the shear capacity of deep RC beams without stirrups. To evaluate the performance of the TENN model, three baseline models are developed and examined across multiple data availability levels. The novel TENN model outperforms the baseline models, particularly when trained on a very limited dataset. Furthermore, the proposed algorithm achieves a higher accuracy than the currently accepted design standards in accurately predicting deep RC beams' shear capacity and demonstrates the capabilities of the TENN model to extrapolate in other domains where large-scale or physical testing is cost-prohibitive.     

基于机器学习的区域火灾分布特征分析方法

为定量分析区域的经济发展水平、人口密度、建筑密集度、消防站分布等因素与火灾发生的关系,引入多种机器学习分类算法进行研究。利用ArcGIS 10.2对非数值型数据进行处理,并根据渔网点内火灾核密度的高低进行等级划分,使变量转化成对应的数值型数据;在确保精度的条件下,利用多次随机森林算法进行特征筛选,并对筛选后的剩余特征进行深度学习训练,同时采用支持向量机算法对所有特征进行训练,并分别构建预测模型;最终将3种算法进行加权平均融合,并通过对比4种模型ROC曲线及分类的准确度进行相应分析。以重庆市火灾警情系统中统计的真实火灾数据为例进行分析的结果显示,4种模型的准确率均高于90%;3种算法耦合后模型准确度和Kappa值分别为0.980 7和0.843 6,其结果与3种单一模型相比较为稳定准确。     

Adaptive Markov chain Monte Carlo algorithms for Bayesian inference: recent advances and comparative study

Abstract

Condition assessments of structures require prediction models such as empirical model and numerical simulation model. Generally, these prediction models have model parameters to be estimated from experimental data. Bayesian inference is the formal statistical framework to estimate the model parameters and their uncertainties. As a result, uncertainties associated with the model and measurement can be accounted for decision making. Markov Chain Monte Carlo (MCMC) algorithms have been widely employed. However, there still remain some implementation issues from the inappropriate selection of the proposal mechanism in Markov chain. Since the posterior density for a given problem is often problem-dependent and unknown, users require a trial-and-error approach to select and tune optimal proposal mechanism. To relieve this difficulty, various adaptive MCMC algorithms have been recently appeared. Users must understand their mechanism and limitations before applying the algorithms to their problems. However, there is no comprehensive work to provide detailed exposition and their performance comparison together. This study aims to bring together different adaptive MCMC algorithms with the goal of providing their mechanisms and evaluating their performances through comparative study. Three algorithms are chosen as the representative proposal mechanism. From comparative studies, the discussions were drawn in terms of performances, simplicity and computational costs for less-experienced users.     

A model of reaeration rates driven by shear flows

The reaeration rate determines the speed that the dissolved oxygen is restored to the saturation level. The reaeration rate is determined by the surface renewal rates from the friction interfaces in the water bodies, including the water/bed interface, the shear‐flow interface and the air/water interface. The formulae of reaeration rate for the air/water interface and the water/bed interface were developed in prior studies. However, no formula of the reaeration rate driven by the shear flows was developed. In this study, a mechanic model of the reaeration rate driven by the shear flows is developed to fill in the gap. The flow velocity profile in the shear flows and the Surface Renewal Theory are employed to derive the corresponding model. The predictions of the formulae for these three types of friction interfaces are compared for the same phase velocity to investigate the reasonability of the reaeration rate model for the shear‐flow interface. The predictions of the model for the shear‐flow interface are between those for the air/water interface and for the water/bed interface. The model in this study is also verified to have reasonable agreements with the experimental data. The model developed in this study can be applied for the prediction of the low soluble gases’ transfer rate between air and water in shear flows.     

Evaluation of Undrained Shear Strength of Soils from Field Penetration Tests

The evaluation of undrained shear strength of soils is necessary in determining the possibility of occurrence of flow deformation during earthquakes. The present study is aimed at examining the evaluation of undrained shear strength of silty sands from field with Swedish weight sounding tests and cone penetration tests. Based on the outcome of the previous studies on laboratory triaxial tests, the undrained shear strength ratio is defined as the undrained shear strength divided by the initial effective major principal stress. The undrained shear strength ratio is then formulated with respect to the relative density. The penetration resistances of Swedish weight sounding and cone penetration tests are then formulated with respect to the effective overburden stress and relative density, based on laboratory calibration chamber tests. By combining these formulations, the correlations of the undrained shear strength with Swedish penetration resistance and cone tip resistance are established. The range of values of penetration resistances indicative of soil layers susceptible to flow deformation is discussed. The correlations of the undrained shear strength with field penetration resistances thus derived are then examined from case history studies. Two case history studies are carried out with Swedish weight sounding tests at the sites of flow failures induced during the recent earthquakes. A series of case history studies are reexamined, which were carried out with Dutch cone penetration tests in the past studies.     

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and support vector machine

Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.     

砂土剪胀剪缩特性及其对抗剪强度的影响

为了研究饱和砂土的剪胀剪缩特性及其对抗剪强度的影响,选取滹沱河细砂,利用空心圆柱扭剪仪较系统地开展了一系列不同初始密度、不同固结压力条件下的排水与不排水纯扭剪试验研究,在总应力保持不变的情况下研究了砂土的剪胀剪缩特性,着重探讨了在排水与不排水试验中,不同密度和不同有效围压的砂土在单调剪切荷载作用下的应力-应变关系、硬化与软化、土体的剪胀剪缩以及强度等特性。结果表明:砂土密度和固结压力对砂土剪胀剪缩特性具有显著的影响;砂土的剪胀剪缩特性对砂土的排水、不排水强度以及应力-应变关系产生显著的影响;由于剪胀剪缩特性的影响,砂土的不排水抗剪强度甚至可能高于排水抗剪强度;研究成果可为今后砂土的本构模型和数值模拟提供试验资料。     

南海北部陆坡软黏土全流动强度试验研究

鉴于海底软黏土强度测试困难和精度不足的现状,研发了一种适用于低强度、高含水率土体强度测试的新型全流动贯入仪,并进行了有效性校验。在此基础上,针对南海北部陆坡区典型软黏土,开展了多组原状试样的全流动强度试验,分析了试验中初始阻力系数N与重塑阻力系数N_(rem)的取值范围,给出了软黏土扰动前后不排水剪切强度沿深度的分布特征及变化趋势,并结合微观孔隙面积比和宏观构造灵敏度,探究了研究区土体的强结构特征。最后,基于重塑不排水剪切强度与含水率/液限间的关联性分析,提出了适用于研究区土体的不排水剪切强度归一化模型,为南海北部陆坡区海底能源开发、海洋工程基础设计与地质灾害预测提供参考。     

Shear strength behavior of clayey soil reinforced with polypropylene fibers under drained and undrained conditions

The fundamental mechanisms controlling shear strength and deformability behavior of clay-fiber mixtures have still not been well established, nor the constraints that may affect their performance of shearing under different drainage conditions. This study aims to understand the behavior of a clay soil mixed with polypropylene fibers using results from drained and undrained triaxial compression tests, and to provide necessary calibration data for a shear strength prediction model. In drained tests, shear strength increased with fiber inclusion for a given mean effective stress, represented by an increase in apparent cohesion. In the undrained tests, the shear strength was not affected by pore water pressure generation. Results from the drained and undrained tests indicate that the fiber content had a greater influence on the apparent cohesion than on the friction angle. Drainage affected the improvement in the peak shear strength of fiber-reinforced soils, with superior improvement in the drained tests. As the percent improvement in shear strength decreased with increasing effective confining stresses for both tests, the difference in behavior in the drained and undrained tests was attributed to the strain at failure, with failure occurring at large strains in the drained tests but at smaller strains in the undrained tests.     

Shear strength of slender reinforced concrete beams without web reinforcement: A model using fuzzy set theory

A reliable shear strength model for slender reinforced concrete beams without web reinforcement is described based on fuzzy set theory. The fuzzy-based model was developed to consider the interaction between the shear modeling parameters and the random and non-random uncertainties in these parameters. The parameters were identified essential for modeling shear strength in slender reinforced concrete beams without web reinforcement being: the compressive strength, the effective depth and the tension reinforcement ratio. A total of 385 experimental datasets obtained from shear tests of simply supported reinforced concrete beams from the literature, are used in learning/developing and verification of the proposed model (164 for learning and 221 for verification). The shear strength predicted by the fuzzy-based model was compared to those predicted by current shear strength models suggested by design codes such as the Eurocode 2 (EC2), the American code ACI (318-05), and Canadian code (CSA A23.3-04). The fuzzy-based model yields a significant enhancement in the prediction of the shear strength while still respecting principles of mechanics governing shear failure in concrete beams.