Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

The Effect of Jet Grouting on Enhancing the Lateral Behavior of Piled Raft Foundation in Soft Clay(Numerical Investigation)

Soft clay soils cannot usually support large lateral loads,so clay soils must be improved to increase lateral resistance.The jet grouting method is one of the methods used to improve weak soils.In this paper,a series of 3D finite element studies were conducted using Plaxis 3D software to investigate the lateral behavior of piled rafts in improved soft clay utilizing the jet grouting method.Parametric models were analyzed to explore the influence of the width,depth,and location of the grouted clay on the lateral resistance.Additionally,the effect of vertical loads on the lateral behavior of piled rafts in grouted clay was also investigated.The numerical results indicate that the lateral resistance increases by increasing the dimensions of the jet grouting beneath and around the piled raft.Typical increases in lateral resistance are 11.2%,65%,177%,and 35%for applying jet grouting beside the raft,below the raft,below and around the raft,and grouted strips parallel to lateral loads,respectively.It was also found that increasing the depth of grouted clay enhances lateral resistance up to a certain depth,about 6 to 10 times the pile diameter(6 to 10D).In contrast,the improvement ratio is limited beyond 10D.Furthermore,the results demonstrate that the presence of vertical loads has a significant impact on sideward resistance.

An Overview of Soil Improvement through Ground Grouting

Soil is an essential component of what surrounds us in nature, providing as the basis for our infrastructure and construction. However, soil is not always suitable for construction due to a variety of geotechnical issues such as inadequate bearing capacity, excessive settlement, and liquefaction susceptibility. Through improving the engineering qualities of soil, such as strength, permeability, and stability, ground grouting is a specific geotechnical method used. Using a fluid grout mixture injected into the subsurface, holes are filled and weak or loose strata are solidified as the material seeps into the soil matrix. The approach’s adaptability in addressing soil-related issues has made it more well-known in the fields of civil engineering and construction. In the end, this has improved groundwater management, foundation support, and overall geotechnical performance.

Effects of jet grouting slabs on responses for deep braced excavations

Jet grouting slabs are widely used in deep excavations owing to their effectiveness for reducing the deflection of the diaphragm wall and the prop forces acting on the struts and improving the basal-heave stability.In this paper,according to case histories in Singapore,a series of finite-element numerical simulations are performed to evaluate the effects of jet grouting slabs on responses to deep braced excavations.On the basis of a parametric sensitivity study,a reasonable thickness of jet grouting slabs is proposed.The effects of the wall depth,wall stiffness,soft-clay thickness,and stiffness on the performance of the jet grouting slabs are assessed by comparing and analyzing a series of simulation results.It is found that the soft-clay thickness significantly affects the wall deflection and basal heave in deep excavation.During the design of support structures,soil profiles should be considered first.The findings of this study provide a reference and guidance for the support system design of similar projects.

Artificial neural network optimized by differential evolution for predicting diameters of jet grouted columns

A novel and effective artificial neural network(ANN) optimized using differential evolution(DE) is first introduced to provide a robust and reliable forecasting of jet grouted column diameters.The proposed computational method adopts the DE algorithm to tackle the difficulties in the training and performance of neural networks and optimize the four quintessential hyper-parameters(i.e.the epoch size,the number of neurons in a hidden layer,the number of hidden layers,and the regularization parameter) that govern the neural network efficacy.This approach is further enhanced by a stochastic gradient optimization algorithm to allow ’expensive’ computation efforts.The ANN-DE is first trained using a prepared jet grouting dataset,then verified and compared with the prevalent machine learning tools,i.e.neural networks and support vector machine(SVM).The results show that,the ANN-DE outperforms the existing methods for predicting the diameter of jet grouting columns since it well balances training efficiency and model performance.Specifically,the ANN-DE achieved root mean square error(RMSE)values of 0.90603 and 0.92813 for the training and testing phases,respectively.The corresponding values were 0.8905 and 0.9006 for the optimized ANN,then,0.87569 and 0.89968 for the optimized SVM,respectively.The proposed paradigm is bound to be useful for solving various geotechnical engineering problems regardless of multi-dimension and nonlinearity.

Seismic Performance Evaluation of Improved Jet Grouted Grid Form with Horizontal Slab in Liquefaction Mitigation

A chain event of the 2016 Kumamoto earthquakes caused considerable geotechnical damage related to liquefaction in many places around Kumamoto plain. Many low-rise houses and traditional Japanese style houses, which were constructed on shallow foundation, suffered differential settlement and tilting due to liquefaction. To mitigate the building damages due to the liquefaction, a new countermeasure method of jet grout grid form with a horizontal slab is introduced in this study. The effectiveness of the proposed technique was evaluated through physical modelling and numerical modelling. As a part of the physical modelling, a set of 1 g shaking table tests for unimproved case and improved case were performed, in which the mitigation effects of the grid form with a horizontal reinforcing slab were examined based on the acceleration, excess pore water pressure ratio as well as ground settlement. Numerical simulation was also performed for assessing the effect of improved method on soil-structure interaction and building settlement during the earthquake. The physical and numerical results confirmed that the grid form with horizontal slab reinforced method is effective in settlement control and offers favorable contribution in liquefaction mitigation.

Development of ensemble learning models to evaluate the strength of coal-grout materials

In the loose and fractured coal seam with particularly low uniaxial compressive strength(UCS),driving a roadway is extremely difficult as roof falling and wall spalling occur frequently.To address this issue,the jet grouting(JG)technique(high-pressure grout mixed with coal particles)was first introduced in this study to improve the self-supporting ability of coal mass.To evaluate the strength of the jet-grouted coal-grout composite(JG composite),the UCS evolution patterns were analyzed by preparing 405 specimens combining the influential variables of grout types,curing time,and coal to grout(C/G)ratio.Furthermore,the relationships between UCS and these influencing variables were modeled using ensemble learning methods i.e.gradient boosted regression tree(GBRT)and random forest(RF)with their hyperparameters tuned by the particle swarm optimization(PSO).The results showed that the chemical grout composite has higher short-term strength,while the cement grout composite can achieve more stable strength in the long term.The PSO-GBRT and PSO-RF models can both achieve high prediction accuracy.Also,the variable importance analysis demonstrated that the grout type and curing time should be considered carefully.This study provides a robust intelligent model for predicting UCS of JG composites,which boosts JG design in the field.

Prevention and treatment technologies of railway tunnel water inrush and mud gushing in China

Water inrush and mud gushing are one of the biggest hazards in tunnel construction. Unfavorable geological sections can be observed in almost all railway tunnels under construction or to be constructed, and vary in extent. Furthermore, due to the different heights of mountains and the lengths of tunnels, the locations of the unfavorable geological sections cannot be fully determined before construction, which increases the risk of water inrush and mud gushing. Based on numerous cases of water inrush and mud gushing in railway tunnels, the paper tries to classify water inrush and mud gushing in railway tunnels in view of the conditions of the surrounding rocks and meteorological factors associated with tunnel excavation. In addition, the causes of water inrush and mud gushing in combination of macroand micromechanisms are summarized, and site-specifc treatment method is put forward. The treatment methods include choosing a method of advance geological forecast according to risk degrees of different sections in the tunnel, determining the items of predictions, and choosing the appropriate methods, i.e. draining-oriented method, blocking-oriented method or draining-and-blocking method. The treatment technologies of railway water inrush and mud gushing are also summarized, including energy relief and pressure relief technology, advance grouting technology, and advance jet grouting technology associated with their key technical features and applicable conditions. The results in terms of treatment methods can provide reference to the prevention and treatment of tunnel water inrush and mud gushing.

Predicting jet-grout column diameter to mitigate the environmental impact using an artificial intelligence algorithm

This paper describes an approach for predicting the diameter of a jet-grout column using the support vector regression(SVR)technique,which is regarded as a novel learning machine based upon recent advances in statistical theory,in which the combined effects of the construction(construction methods and jetting parameters)and soil properties(soil type and shearing resistance)are considered.Four different kernel functions,namely,a linear kernel function,polynomial kernel function,radial basis kernel function,and sigmoid kernel function,are integrated into the SVR technique.A large amount of field measured data on the diameter of jet-grout column are retrieved from the published literature for training and testing purposes.The results indicate that the SVR technique with a radial basis kernel function provides predictions closest to the measured results,whereas the prepared design charts enable the ability to significantly widen the application of the proposed approach to the areas of ground improvement and environmental protection.