Experimental and numerical modeling on vacuum consolidation behavior of staged-filled soil slurry with prefabricated horizontal drain and flocculant

The vacuum-assisted prefabricated horizontal drain offers a promising method for strengthening soil slurry,allowing simultaneous filling and vacuum-dewatering via staged construction.However,there is limited research on the unique characteristics of staged filling.This study aims to investigate the vacuum consolidation process of staged-filled soil slurry through laboratory model tests and numerical simulations,also assessing the impact of anionic polyacrylamide.Comparative analyses are conducted between vacuum consolidation with and without anionic polyacrylamide,as well as self-weight consolidation without anionic polyacrylamide.Results reveal contour lines of excess pore pressure,water content,and soil strength forming an ellipse around the prefabricated horizontal drain board.During the consolidation process,a higher degree of consolidation,lower water content,and higher soil strength were observed closer to the prefabricated horizontal drain board.After treatment,the uppermost filling layer exhibits an average water content that was approximately 40%higher than the lower filling layer,and its average strength was about 60%lower.This discrepancy is primarily due to the absence of sealing on the top surface and the relatively short vacuum consolidation time caused by staged filling.The introduction of anionic polyacrylamide-induced flocculation significantly improves the initial consolidation rate but minimally affects the dewatering capacity of vacuum preloading.Using flocculant can enhance both the staged filling rate and soil strength(by 1e2 times).Additionally,employing a staggered arrangement between different prefabricated horizontal drain layers is advisable to prevent top-down penetration in areas with low soil strength.

Effects of synthetic site water on bentonite-concrete system for a potential nuclear waste repository

In high-level nuclear waste(HLW)repositories,concrete and compacted bentonite are designed to be employed as buffer materials,which may raise a problem of interactions between concrete and bentonite.These interactions would lead to mineralogy transformation and buffer performance decay of bentonite under the near field environment conditions in a repository.A small-scale experimental setup was established to simulate the concrete-bentonite-site water interaction system from a potential nuclear waste repository in China.Three types of mortars were prepared to correspond to the concrete at different degradation states.The results permit the determination of the following:(1)The macroproperties of Gaomiaozi(GMZ)bentonite(e.g.swelling pressure,permeability,the final dry density,and water content of reacted samples);(2)The composition evolution of fluids from the synthetic site water-concrete-bentonite interaction systems;(3)The sample characterization including Fourier transform infrared spectroscopy(FTIR)and X-ray powder diffraction(XRD).Under the infiltration of the synthesis Beishan site water(BSW),the swelling pressure of bentonite decreases slowly with time after reaching its second swelling peak.The flux decreases with time during the infiltrations,and it tends to be stable after more than 120 d.Due to the cation exchange reactions in the BSW-concrete-bentonite systems,the divalent cations(Ca and Mg)were consumed,and the monovalent cations(Na and K)were released.The dissolution of minerals in the bentonite such as albite causes Si increasing in the pore water.It was concluded that the hydro-mechanical property degradation of bentonite takes place when it comes into contact with concrete mortar,even under low-pH groundwater conditions.The soil dispersion,the uneven water content,and the uneven dry density in bentonite samples may partly contribute to the swelling decay of bentonite.Therefore,the direct contact with concrete has an obvious effect on the performance of bentonite.

Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network

Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cutter life(Hf)by integrating a group method of data handling(GMDH)-type neural network(NN)with a genetic algorithm(GA).The efficiency and effectiveness of the GMDH network structure are optimized by the GA,which enables each neuron to search for its optimum connections set from the previous layer.With the proposed model,monitoring data including the shield performance database,disc cutter consumption,geological conditions,and operational parameters can be analyzed.To verify the performance of the proposed model,a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model.The results indicate that the hybrid model predicts disc cutter life with high accuracy.The sensitivity analysis reveals that the penetration rate(PR)has a significant influence on disc cutter life.The results of this study can be beneficial in both the planning and construction stages of shield tunneling.

Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a challenge.This paper suggests a novel modelling approach using machine learning(ML)technique.The performance of five commonly used machine learning(ML)algorithms,i.e.back-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,i.e.initial void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first established.Genetic algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML models.The results indicate that ML models outperform empirical prediction formulations with lower prediction error.RF yields the lowest error followed by BPNN,ELM,EPR and SVM.If the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict Cc.Furthermore,if the distribution of input variables is continuous,RF model is the best one.Otherwise,EPR model is recommended if the ranges of input variables are small.The predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.

Micromechanical analysis of the behavior of stiffclay

Cementations formed in geological timescale are observed in various stiff clays.A micromechanical stress strain model is developed for modeling the effect of cementation on the deformation behavior of stiff clay.The proposed approach considers explicitly cementations at intercluster contacts,which is different from conventional model.The concept of inter-cluster bonding is introduced to account for an additional cohesion in shear sliding and a higher yield stress in normal compression.A damage law for inter-cluster bonding is proposed at cluster contacts for the debonding process during mechanical loading.The model is used to simulate numerous stress-path tests on Vallericca stiff clay.The applicability of the present model is evaluated through comparisons between the predicted and the measured results.In order to explain the stress-induced anisotropy arising from externally applied load,the evolution of local stresses and local strains at inter-cluster planes are discussed.

Durable response to pulsatile icotinib for central nervous system metastases from EGFR-mutated non-small cell lung cancer: A case report

BACKGROUND Central nervous system(CNS) metastases are a catastrophic complication of nonsmall cell lung cancer(NSCLC), including brain and leptomeningeal carcinomatosis, and are always accompanied by a poor prognosis. Despite the continuous development of existing treatments, the therapy of CNS metastases remains challenging.CASE SUMMARY We report a patient who was definitively diagnosed with brain and leptomeningeal metastases from NSCLC with a targeted mutation in epidermal growth factor receptor(EGFR). A standard dosage of icotinib(125 mg three times daily) was implemented but ineffective. CNS lesions developed despite stable systemic control, so pulsatile icotinib(1125 mg every 3 d) was administered. This new strategy for administration has lasted 25 mo so far, and resulted in complete remission of neurological symptoms, almost vanished lesions, and longer survival with no notable side effects.CONCLUSION This is the first successful example of pulsatile icotinib for treating isolated CNS progression from EGFR mutation-positive NSCLC, providing a new alternative for the local treatment of CNS metastases.

Incorporating mitigation strategies in machine learning for landslide susceptibility prediction

This study proposes an approach that considers mitigation strategies in predicting landslide susceptibility through machine learning(ML)and geographic information system(GIS)techniques.ML models,such as random forest(RF),logistic regression(LR),and support vector classification(SVC)are incorporated into GIS to predict landslide susceptibilities in Hong Kong.To consider the effect of mitigation strategies on landslide susceptibility,non-landslide samples were produced in the upgraded area and added to randomly created samples to serve as ML models in training datasets.Two scenarios were created to compare and demonstrate the efficiency of the proposed approach;Scenario I does not considering landslide control while Scenario II considers mitigation strategies for landslide control.The largest landslide susceptibilities are 0.967(from RF),followed by 0.936(from LR)and 0.902(from SVC)in Scenario II;in Scenario I,they are 0.986(from RF),0.955(from LR)and 0.947(from SVC).This proves that the ML models considering mitigation strategies can decrease the current landslide susceptibilities.The comparison between the different ML models shows that RF performed better than LR and SVC,and provides the best prediction of the spatial distribution of landslide susceptibilities.

Monotonic mechanical behaviour of compacted completely decomposed granite with various inclusion levels of incineration bottom ash

1 Introduction Completely decomposed granite(CDG)is widely distributed in South China(Xu et al.,2022;Wang et al.,2023).The parent granite rock mass gradually loses features during the weathering process,and thus needs to be reinforced when used(Lan et al.,2003;Dassekpo et al.,2017;Alamanis et al.,2021).On the other hand,considering the huge demand for construction materials and limited natural resources,it is increasingly important to fully utilize solid waste(Gruhler et al.,2019;Anagnostopoulos et al.,2020;Jiang et al.,2022,2023a,2023b).With high strength and environment-friendly characteristics,incineration bottom ash(IBA)seems to be a suitable reinforcement material for CDG(Ahmed and Khalid,2011;Alhassan and Tankó,2012;Toraldo et al.,2013;Lynn et al.,2017;Xuan et al.,2018;Tang et al.,2020).

MODELLING STRAIN-RATE-DEPENDENCY OF NATURAL SOFT CLAYS COMBINED WITH ANISOTROPY AND DESTRUCTURATION

The paper aims to investigate modelling the strain-rate-dependency of natural soft clays combined with anisotropy and destructuration using an elasto-viscoplastic model. The model is based on Perzyna＇s overstress theory and the elastoplastic model S-CLAYIS. Tests at constant strain^rate and creep tests under both one-dimensional and triaxial conditions on several clays are simulated. Simulations highlight the loading scenarios in which it is necessary to account for anisotropy and/or destructuration in order to get accurate predictions. Comparisons between the predicted and measured results demonstrate that the proposed model can successfully reproduce the time-dependent behaviour of natural soft clays under different loading conditions.

Analysis of cement-treated clay behavior by micromechanical approach

Experimental results show the significant influence of cement content on the mechanical properties of cement-treated clays.Cementation is produced by mixing a certain amount of cement with the saturated clay.The purpose of this paper is to model the cementation effect on the mechanical behavior of cement-treated clay.A micromechanical stress-strain model is developed considering explicitly the cementation at inter-cluster contacts.The inter-cluster bonding and debonding during mechanical loading are introduced in two ways:an additional cohesion in the shear sliding and a higher yield stress in normal compression.The model is used to simulate isotropic compression and undrained triaxial tests under various confining stresses on cement-treated Ariake clay and Singapore clay with various cement contents.The applicability of the present model is evaluated through comparisons between numerical and experimental results.The evolution of local stresses and local strains in inter-cluster planes are discussed in order to explain the induced anisotropy due to debonding at contact level under the applied loads.The numerical simulations demonstrate that the proposed micromechanical approach is well adapted for taking into account the main physical properties of cement-treated clay,including damage and induced anisotropy under mechanical loading.

Large deformation analysis in geohazards and geotechnics

For geohazards and geotechnics,numerous problems involve large deformation,such as the installation of foundations(Jin YF et al.,2018a),landslides(Jin YF et al.,2020b),debris flow(Dai et al.,2017),collapse of underground structures(Zhang et al.,2019),and the formation of sinkholes(Baran-diaran Villegas,2018).Benefitting from the sustained development of computing power,numerical simulations have become useful analytical methods in geomechanics and related fields.

Practice of artificial intelligence in geotechnical engineering

Geotechnical engineering deals with materials(e.g.soil and rock)that,by their very nature,exhibit varied and uncertain behavior due to the imprecise physical processes associated with their formation(Mitchell and Soga,2005).Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the ability of most traditional forms of physically-based engineering methods.In recent years,the application of artificial intelligence(AI)in a wide range of geotechnical engineering has grown rapidly(Nawari et al.,1999;Miranda,2007;Javadi and Rezania,2009;Shahin,2013,2016;Chen et al.,2018;Yin et al.,2018;Jin et al.,2019a,2019b,2019c;Zhang P et al.,2020a).

Influence of natural deposition plane orientation on oedometric consolidation behavior of three typical clays from southeast coast of China

The parameters obtained from oedometric consolidation tests are commonly used in the development of constitutive modeling and for engineering practice. This paper focuses on the influence of the natural deposition plane orientation on oedometric consolidation behavior of three natural clays from the southeast coast of China. Oedometer tests were conducted on intact specimens prepared by sampling at a series of angles relative to the natural deposition plane. For each specimen, yield stress,compressibility indexes, secondary compression, and permeability coefficients were determined. The influence of the sampling angle on these properties was investigated, revealing that yield stress, compression index, swelling index, creep index, ratio of secondary compression coefficient to compression index(Cae/Cc) and permeability coefficient were all dependent to some extent on the sampling angle. These findings indicate the role of the anisotropy due to the natural deposition on the oedometric consolidation behavior.

Enhanced elastic beam model with BADS integrated for settlement assessment of immersed tunnels

Excessive settlement may induce structural damage and water leakage in immersed tunnels,seriously threatening the tunnels’safety.However,making accurate assessment of the settlement in immersed tunnels is difficult due to the incomplete knowledge of the geotechnical parameters and the inadequacy of the model itself.This paper proposes an effective method to accurately assess the settlement in immersed tunnels.An enhanced beam on elastic foundation model(E-BEFM)is developed for the settlement assessment,with the Bayesian adaptive direct search algorithm adopted to estimate unknown model parameters based on previous observations.The proposed method is applied to a field case of the Hong Kong–Zhuhai–Macao immersed tunnel.The original BEFM is used for comparison to highlight the better assessment performance of E-BEFM,particularly for joints’differential settlement.Results show that the proposed method can provide accurate predictions of the total settlement,angular distortion(a representation of tubes’relatively differential settlement),and joints’differential settlement,which consequently supports the associated maintenance decision-making and potential risk prevention for immersed tunnels in service.

Physical model testing in geotechnical engineering

1Introduction Several characteristics of natural soils complicate the relationship between their mechanical behaviour and geotechnical construction and maintenance in the field.These characteristics include the presence of three phases(solid particle,water,and air),particle constitutions of various minerals(such as quartz,kaolinite,and montmorillonite),and an exceptionally wide range of particle size fromμm-scale(clay particles smallerthan2μm)to100-mm scale(suchas somegravelsandpebbles)。