Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

In this study, th e least sq u are su p p o rt v ecto r m achine （LSSVM） alg o rith m w as applied to predicting th ebearing capacity o f b ored piles e m b ed d ed in sand an d m ixed soils. Pile g eo m etry an d cone p e n e tra tio nte s t （CPT） resu lts w ere used as in p u t variables for pred ictio n o f pile bearin g capacity. The d ata u se d w erecollected from th e existing litera tu re an d consisted o f 50 case records. The application o f LSSVM w ascarried o u t by dividing th e d ata into th re e se ts： a train in g se t for learning th e pro b lem an d obtain in g arelationship b e tw e e n in p u t variables an d pile bearin g capacity, and testin g an d validation sets forevaluation o f th e predictive an d g en eralization ability o f th e o b tain ed relationship. The predictions o f pilebearing capacity by LSSVM w ere evaluated by com paring w ith ex p erim en tal d ata an d w ith th o se bytrad itio n al CPT-based m eth o d s and th e gene ex pression pro g ram m in g （GEP） m odel. It w as found th a t th eLSSVM perform s w ell w ith coefficient o f d eterm in atio n , m ean, an d sta n d ard dev iatio n equivalent to 0.99,1.03, an d 0.08, respectively, for th e testin g set, an d 1, 1.04, an d 0.11, respectively, for th e v alidation set. Thelow values o f th e calculated m ean squared e rro r an d m ean ab so lu te e rro r indicated th a t th e LSSVM w asaccurate in p redicting th e pile bearing capacity. The results o f com parison also show ed th a t th e p roposedalg o rith m p red icted th e pile bearin g capacity m ore accurately th a n th e trad itio n al m eth o d s including th eGEP m odel.

Adaptive sampling strategy for characterizing spatial distribution of soil liquefaction potential using cone penetration test

Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineering practice,soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests,e.g.cone penetration tests(CPTs),due to the restrictions of time,cost and access to subsurface space.In these cases,liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method,leading to remarkable statistical uncertainty in liquefaction assessment.This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment.To tackle this issue,this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a selfadaptive and data-driven manner.The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling(BCS).Both simulated and real CPT data are used to demonstrate the proposed method.Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.

Seasonal influence on cone penetration test: An unsaturated soil site example

Interpretation of electric cone penetration test(CPT) based pore water pressure measurement(CPTu) is well established for soils with behavior that follows classical soil mechanics. The literature on the interpretation of these tests performed on unsaturated tropical soils is limited, and little is known about the influence of soil suction on in situ test data. In this context, the CPT data are presented and discussed to illustrate the seasonal variability in an unsaturated tropical soil site. The test data show that soil suction significantly influenced CPT data up to a depth of 4 m at the study site. It shows the importance of considering seasonal variability in unsaturated soil sites caused by soil suction, which was related to water content through a soil-water retention curve(SWRC). It is also important to consider this aspect in the interpretation of CPT data from these soils.

Technique for Estimating the Cone Bearing Smoothing Parameters

Cone penetration testing (CPT) is an extensively utilized and cost effective tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic cone into penetrable soils and recording the resistance to the cone tip (qc value). The measured qc values (after correction for the pore water pressure) are utilized to estimate soil type and associated soil properties based predominantly on empirical correlations. The most common cone tips have associated areas of 10 cm2 and 15 cm2. Investigators also utilized significantly larger cone tips (33 cm2 and 40 cm2) so that gravelly soils can be penetrated. Small cone tips (2 cm2 and 5 cm2) are utilized for shallow soil investigations. The cone tip resistance measured at a particular depth is affected by the values above and below the depth of interest which results in a smoothing or blurring of the true bearing values. Extensive work has been carried out in mathematically modelling the smoothing function which results in the blurred cone bearing measurements. This paper outlines a technique which facilitates estimating the dominant parameters of the cone smoothing function from processing real cone bearing data sets. This cone calibration technique is referred to as the so-called CPSPE algorithm. The mathematical details of the CPSPE algorithm are outlined in this paper along with the results from a challenging test bed simulation.

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Rolling dynamic compaction(RDC),which involves the towing of a noncircular module,is now widespread and accepted among many other soil compaction methods.However,to date,there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC.This study presents the application of artificial neural networks(ANNs) for a priori prediction of the effectiveness of RDC.The models are trained with in situ dynamic cone penetration(DCP) test data obtained from previous civil projects associated with the 4-sided impact roller.The predictions from the ANN models are in good agreement with the measured field data,as indicated by the model correlation coefficient of approximately 0.8.It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types.

Cone Bearing Estimation Utilizing a Hybrid HMM and IFM Smoother Filter Formulation

Cone penetration testing (CPT) is a widely used geotechnical engineering in-situ test for mapping soil profiles and assessing soil properties. In CPT, a cone on the end of a series of rods is pushed into the ground at a constant rate and resistance to the cone tip is measured (qm). The qm values are utilized to characterize the soil profile. Unfortunately, the measured cone tip resistance is blurred and/or averaged which can result in the distortion of the soil profile characterization and the inability to identify thin layers. This paper outlines a novel and highly effective algorithm for obtaining cone bearing estimates qt from averaged or smoothed qm measurements. This qt optimal filter estimation technique is referred to as the qtHMM-IFM algorithm and it implements a hybrid hidden Markov model and iterative forward modelling technique. The mathematical details of the qtHMM-IFM algorithm are outlined in this paper along with the results from challenging test bed. The test bed simulations have demonstrated that the qtHMM-IFM algorithm can derive accurate qt values from challenging averaged qm profiles. This allows for greater soil resolution and the identification and quantification of thin layers in a soil profile.

Methodology for Obtaining Optimal Sleeve Friction and Friction Ratio Estimates from CPT Data

Cone penetration testing (CPT) is a cost effective and popular tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic penetrometer into penetrable soils and recording cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) with depth. The measured qc, fs and u values are utilized to estimate soil type and associated soil properties. A popular method to estimate soil type from CPT measurements is the Soil Behavior Type (SBT) chart. The SBT plots cone resistance vs friction ratio, Rf [where: Rf = (fs/qc)100%]. There are distortions in the CPT measurements which can result in erroneous SBT plots. Cone bearing measurements at a specific depth are blurred or averaged due to qc values being strongly influenced by soils within 10 to 30 cone diameters from the cone tip. The qcHMM algorithm was developed to address the qc blurring/averaging limitation. This paper describes the distortions which occur when obtaining sleeve friction measurements which can in association with qc blurring result in significant errors in the calculated Rf values. This paper outlines a novel and highly effective algorithm for obtaining accurate sleeve friction and friction ratio estimates. The fc optimal filter estimation technique is referred to as the OSFE-IFM algorithm. The mathematical details of the OSFE-IFM algorithm are outlined in this paper along with the results from a challenging test bed simulation. The test bed simulation demonstrates that the OSFE-IFM algorithm derives accurate estimates of sleeve friction from measured values. Optimal estimates of cone bearing and sleeve friction result in accurate Rf values and subsequent accurate estimates of soil behavior type.

Seismic liquefaction potential assessment by using relevance vector machine

Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine （RVM） to determine the liquefaction potential of soil by using actual cone penetration test （CPT） data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network （ANN） model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.

Liquefaction prediction using support vector machine model based on cone penetration data

A support vector machine(SVM)model has been developed for the prediction of liquefaction susceptibility as a classification problem,which is an imperative task in earthquake engineering.This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test(CPT)data from the 1999 Chi-Chi,Taiwan earthquake.The SVM,a novel learning machine based on statistical theory,uses structural risk minimization(SRM)induction principle to minimize the error.Using cone resistance(q_(c))and cyclic stress ratio(CSR),model has been developed for prediction of liquefaction using SVM.Further an attempt has been made to simplify the model,requiring only two parameters(q_(c)and maximum horizontal acceleration a_(max)),for prediction of liquefaction.Further,developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model.For Chi-Chi earthquake,the model predicts with accuracy of 100%,and in the case of global data,SVM model predicts with accuracy of 89%.The effect of capacity factor(C)on number of support vector and model accuracy has also been investigated.The study shows that SVM can be used as a practical tool for prediction of liquefaction potential,based on field CPT data.

Proposals of SPT-CPT and DPL-CPT correlations for sandy soils in Brazil

Field tests in geotechnical engineering are fundamental for identification of the underground conditions.The standard penetration test(SPT) is the most commonly used geotechnical approach. There has been an increase both in the use and application of the in situ tests: cone penetration test(CPT) and dynamic probing(DP). Several empirical SPT-CPT and dynamic probing light(DPL)-CPT correlations for sandy soils have been discussed in the literature. New SPT-CPT and DPL-CPT correlations for the sandy soils of the city of Vitoria, in the southeast of Brazil, are suggested in this paper. Statistical analyses to evaluate the quality of the data used are performed, and the suggested correlations are validated with several previous published datasets. The paper also provides some insights into SPT-CPT correlations and soil characteristics(i.e. the mean particle size and the fines fraction of the soil).

Assessment of clay stiffness and strength parameters using index properties

A new approach is developed to determine the shear wave velocity in saturated soft to firm clays using measurements of the liquid limit, plastic limit, and natural water content with depth. The shear wave velocity is assessed using the site-specific variation of the natural water content with the effective mean stress. Subsequently, an iterative process is envisaged to obtain the clay stiffness and strength parameters.The at-rest earth pressure coefficient, as well as bearing capacity factor and rigidity index related to the cone penetration test, is also acquired from the analyses. Comparisons are presented between the measured clay parameters and the results of corresponding analyses in five different case studies. It is demonstrated that the presented approach can provide acceptable estimates of saturated clay stiffness and strength parameters. One of the main privileges of the presented methodology is the site-specific procedure developed based on the relationships between clay strength and stiffness parameters, rather than adopting direct correlations. Despite of the utilized iterative processes, the presented approach can be easily implemented using a simple spreadsheet, benefiting both geotechnical researchers and practitioners.

CPT-Based estimation of undrained shear strength of fine-grained soils in the Huanghe River Delta

The Huanghe River(Yellow River)Delta has a wide distribution of fine-grained soils.Fluvial alluviation,erosion,and wave loads affect the shoal area,resulting complex physical and mechanical properties to sensitive finegrained soil located at the river-sea boundary.The cone penetration test(CPT)is a convenient and effective in situ testing method which can accurately identify various soil parameters.Studies on undrained shear strength only roughly determine the fine content(FC)without making the FC effect clear.We studied four stations formed in different the Huanghe River Delta periods.We conducted in situ CPT and corresponding laboratory tests,examined the fine content influence on undrained shear strength(S_(u)),and determined the cone coefficient(N_(k)).The conclusions are as follows.(1)The fine content in the area exceeded 90%,and the silt content was high,accounting for more than 70%of all fine particle compositions.(2)The undrained shear strength gradually increased with depth with a maximum of approximately 250 kPa.When the silt content was lower than 60%–70%,the undrained shear strength decreased.(3)The silt and clay content influenced undrained shear strength,and the fitted f_(s)h/q_(t) function model was established,which could be applied to strata with a high fine content.The cone coefficients were between 20 and 25,and the overconsolidated soil layer had a greater cone coefficient.

A Case Study on Soil Improvement with Rapid Impact Compaction (RIC)

Soil treatment was utilized on numerous production sites to compact cohesion less formations, having the objective to increase earth characteristics and decrease probable subsidence. Within the last few years, Rapid Impact Compaction (RIC) has increased its attractiveness as a soil treatment method. RIC is an innovative dynamic compaction technique primarily used to compact sandy soils where silt and clay contents are low. This work presents a case study of ground improvement using RIC and its suitability for site preparation earthworks. The RIC technique has been performed in an early site preparation which consists of a cut and fill contract for a mega project in the Kingdom of Saudi Arabia. RIC is a process where loose subsurface soils are improved through compaction with the utilization of successive impact blows from the top surface. This project involves the compaction of the fill materials (with an average thickness of 4 m) and loose natural formations (averaging 4 m in depth). The objective of the soil treatment scheme is to increase the relative density of the soils (both fill and natural) to 85%. The usage of the RIC within the site preparation earthwork applications is possible provided the presence of certain elements—specifically, granular materials and particles finer than number 200 sieve—do not exceed 15%. The RIC method proved to be cost- and time-effective when utilized for filling compaction activities since it compacts considerable soil thicknesses with a single action from the top surface, and can be used as an alternative to the traditional method of compacting fill formations in pre-determined lift thicknesses.

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.

A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability:Exploration from historical data

The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability(LLDV)when determining whether liquefaction is likely to cause damage at the ground's surface.This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network(BBN)methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model.The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learming(ML)algorithm-K2 and domain knowledge(DK)data fusion methodology.Compared with the C4.5 decision tree-J48 model,naive Bayesian(NB)classifier,and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen's kappa coefficient,the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage.The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations,and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development.The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling.This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefed sites based on an engineering point of view.