Cosine Amplitude Method Research Articles

Improved determination of the S-wave velocity of rocks in dry and saturated conditions: Application of machine-learning algorithms

The determination of S-wave velocity (Vs) is of significant importance in various engineering disciplines, including mining, civil, and geotechnical engineering. It is beneficial to indirectly determine Vs under both dry and saturated conditions and to understand its relationship with influencing input variables: coring depth (H), durability index (DI), water content (Wa), dry density (ρd), saturated density (ρs), and porosity (n). In this study, we evaluate these relationships using three multiple machine-learning algorithms (MLAs): artificial neural network (ANN), fuzzy inference system (FIS), and gene expression programming (GEP), alongside a linear regression method (LRM) and predict both dry S-wave velocity (Vs-dry) and saturated S-wave velocity (Vs-sat) of rocks. The research involves the analysis of 90 datasets derived from samples of schist, phyllite, and sandstone rocks collected from Azad and Bakhtiari dam sites in Iran. The diversity of these datasets is a key advantage of this study, providing a solid foundation for models training and testing while enhancing the models’ generalizability. Model optimization techniques are employed in the Python, MATLAB, GenXProTools, and SPSS environments to identify the most effective versions of ANN, FIS, GEP, and LRM models, respectively. The prediction performance analysis reveals that all applied models yield acceptable levels of accuracy for predicting Vs-dry and Vs-sat. However, GEP emerges as the best model for predicting both Vs-dry and Vs-sat. The ANN and FIS models also achieve high levels of accuracy, while LRM performs comparatively less well. Additionally, sensitivity analysis conducted using the cosine amplitude method (CAM) highlights the influence of different variables on Vs-dry and Vs-sat. The ρd is found to be the most influential parameter on Vs-dry, whereas DI exhibits the least impact. Conversely, the ρs significantly affects Vs-sat, while Wa shows the lowest impact. The exceptional performance of these proposed MLAs confirms their applicability in real-world rock engineering and geotechnics projects, offering precise determination of Vs. The diversity of studied rock types and datasets, along with the use of cost-effective and easy measurable inputs, the determination of Vs in both dry and saturated status, and the application of robust MLAs for Vs determination are the main novelties of this study. However, further researches involving additional datasets and more rock types are required to validate these findings.

Reliability‐based state parameter liquefaction probability prediction using soft computing techniques

The state parameter (ѱ) accounts for both relative density and effective stress, which influence the cyclic stress or liquefaction characteristic of the soil significantly. This study presents a ѱ‐based probabilistic liquefaction evaluation method using six soft computing (SC) techniques. The liquefaction probability of failure (PL) is calculated using the first‐order second moment (FOSM) method based on the cone penetration test (CPT) database. Then, six SC techniques, such as Gaussian process regression (GPR), relevance vector machine (RVM), functional network (FN), genetic programming (GP), minimax probability machine regression (MPMR) and multivariate adaptive regression splines (MARS), are used to predict PL. The performance of these models is examined using nine statistical indices. Additionally, plots such as regression plots, Taylor diagrams, error matrix and rank analysis are shown to assess the SC model's performance. Finally, sensitivity analysis is performed using the cosine amplitude method (CAM) to assess the influence of input parameters on output. The current study demonstrates that SC models based on state parameter predict PL effectively. RVM and MPMR models closely follow the GPR model in terms of performance, which is superior to the other models. Notably, two equations are generated using GP and MARS models to predict PL. The results of the sensitivity analysis reveal the magnitude of earthquake (Mw) as the most sensitive parameter. The outcomes of this research will offer risk evaluations for geotechnical engineering designs and expand the use of state parameter‐based SC models in liquefaction analysis.

Prediction of HGI of South African coalfields: a comparative application of ANN, SVR and LSTM models

ABSTRACT Hardgrove grindability index (HGI) is a significant index used to determine a mill’s capability and overall efficiency in the grinding of coal. There are several factors that affect HGI due to the complex nature of coal. The influence of proximate analysis, calorific value, total sulfur, and pyrite content on the HGI values of 292 coal samples from South African coalfields were examined. Predictive models of HGI are developed by using soft computing techniques such as Long Short-Term Memory (LSTM), support vector regression (SVR), and Artificial Neural Network (ANN). This study shows that ANN is the most effective predictive model for all the three coalfields, with SVR models being second and LSTM models being the least effective. The correlation between the predicted value and the input data is established by using the cosine amplitude method (CAM). It was found that HGI is most influenced by the fixed carbon content, calorific value, ash content and volatile matter content while pyrite has the least influence.

Predicting dynamic compressive strength of frozen-thawed rocks by characteristic impedance and data-driven methods

In cold regions, the dynamic compressive strength (DCS) of rock damaged by freeze-thaw weathering significantly influences the stability of rock engineering. Nevertheless, testing the dynamic strength under freeze-thaw weathering conditions is often both time-consuming and expensive. Therefore, this study considers the effect of characteristic impedance on DCS and aims to quickly determine the DCS of frozen-thawed rocks through the application of machine-learning techniques. Initially, a database of DCS for frozen-thawed rocks, comprising 216 rock specimens, was compiled. Three external load parameters (freeze-thaw cycle number, confining pressure, and impact pressure) and two rock parameters (characteristic impedance and porosity) were selected as input variables, with DCS as the predicted target. This research optimized the kernel scale, penalty factor, and insensitive loss coefficient of the support vector regression (SVR) model using five swarm intelligent optimization algorithms, leading to the development of five hybrid models. In addition, a statistical DCS prediction equation using multiple linear regression techniques was developed. The performance of the prediction models was comprehensively evaluated using two error indexes and two trend indexes. A sensitivity analysis based on the cosine amplitude method has also been conducted. The results demonstrate that the proposed hybrid SVR-based models consistently provided accurate DCS predictions. Among these models, the SVR model optimized with the chameleon swarm algorithm exhibited the best performance, with metrics indicating its effectiveness, including root mean square error (RMSE) = 3.9675, mean absolute error (MAE) = 2.9673, coefficient of determination (R2) = 0.98631, and variance accounted for (VAF) = 98.634. This suggests that the chameleon swarm algorithm yielded the most optimal results for enhancing SVR models. Notably, impact pressure and characteristic impedance emerged as the two most influential parameters in DCS prediction. This research is anticipated to serve as a reliable reference for estimating the DCS of rocks subjected to freeze-thaw weathering.

Sustainability performance assessment of sago industry supply chain using a multi-criteria adaptive fuzzy inference model

Background: Sustainable supply chains are more competitive than conventional supply chains. Supply chain sustainability performance needs to be carried out to determine sustainability under current conditions and to design appropriate strategies to increase sustainability. This study aims to design a sustainability performance assessment model for the sago agro-industry supply chain and identify critical indicators for sustainability improvement. Methods: The Fuzzy Inference System (FIS) evaluates sustainability on three levels: economic, social, and environmental. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is then used to aggregate the overall sustainability performance. The cosine amplitude method (CAM) was used to analyze key indicators. This study assessed the sustainability performance on industrial- and small-medium-scale sago agro-industry. Results: The results show that the supply chain sustainability performance on the industrial scale is 44.25, while it is 48.81 for the small-medium scale with the same status, almost sustainable. Key indicators for improving sago agro-industry supply chain sustainability performance include profit distribution among supply chain actors, institutional support for supply chains, waste utilization (reuse & recycle), and the availability of waste management facilities. The implication of this research for managers regards assessing the current status of sustainability performance and key indicators as a reference for formulating sustainability strategies and practices. Conclusions: The results of the study will enable supply chain actors to understand the key indicators for improving sustainability performance in the sago agro-industry supply chain, especially in Meranti Islands Regency, Riau Province. The proposed model can be applied to other agro-industries by adjusting the indicators used and assessing data availability and suitability for the research object.

Performance assessment and optimization of liquid desiccant dehumidifier system using intelligent models and integration with solar dryer

Realistic performance predictions are necessary to control the liquid desiccant dehumidification systems effectively. In the present study, the application of artificial intelligence (AI) based artificial neural network (ANN), gene expression program (GEP) and adaptive neuro-fuzzy inference system (ANFIS) is investigated to estimate the dehumidifier effectiveness of desiccant dehumidifier systems while considering the effect of moisture removal rate and sensible heat factor as performance characteristics. It is found that the AI-based GEP model has the best prediction capability compared to the other developed AI models. In addition, the sensitivity analysis of independent parameters on the system performance parameters is estimated using the cosine amplitude method. The results demonstrate that the inlet desiccant temperature and specific humidity have a more substantial influence on the dehumidifier effectiveness and sensible heat ratio. Further, an algorithm involving the combination of multi-objective particle swarm optimization (MOPSO) with the GEP model is developed to optimize the input process parameters and to achieve better dehumidification performance. Lastly, based on obtained optimal operating conditions, a case study on a dehumidifier-integrated solar dryer is proposed for food/agriculture products drying applications using AI-based GEP-MOPSO model. It is also observed that the inlet water temperature and solar intensity have a significant impact on the useful heat gain of the solar collector.

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming.

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19.

An Adaptive Fuzzy Multi-Criteria Model for Sustainability Assessment of Sugarcane Agroindustry Supply Chain

Supply chain sustainability assessment is key to maintaining and improving the performance of agroindustry supply chains, particularly in sustainable agroindustry development. The assessment of agro-industrial supply chain performance is a complex and dynamic process. Hence, there is a need for an adaptive fuzzy multi-criteria sustainability assessment model as an alternative method of analysis and improvement. This study aimed to design an adaptive fuzzy multi-criteria sustainability assessment and improvement model for the sugarcane agroindustry supply chain. In this study: (1) a fuzzy inference system (FIS) was developed to assess the performance of the sustainability dimensions. This study proposed 24 indicators for four dimensions: economic, social, environmental, and resource. (2) An adaptive neuro-fuzzy inference system (ANFIS) is designed to aggregate the overall supply chain sustainability performance. (3) The proposed fuzzy multi-criteria assessment model was compared with the common multidimensional scaling (MDS) and linear models. This study proved that the proposed synthesis of the FIS and ANFIS models is powerful and adaptive for evaluating supply chain sustainability and providing accurate results. (4) The strategies to improve sustainability performance were developed using the cosine amplitude method (CAM). The proposed model determined that the overall supply chain sustainability value was 68.58%, which was almost sustainable. Several strategies have been suggested to improve sustainability performance, including maintaining the sugarcane supply by strengthening the partnership program and improving the mill’s overall recovery, followed by factory revitalization or new factory investment.

A Fuzzy Rule-Based System for Classification of Diabetes.

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Prediction of BlastInduced Ground Vibration (BIGV) of Metro Construction Using Difference Evolution AlgorithmOptimized Gaussian Process (DE-GP)

Rock blasting often has an irreversible impact on the surrounding environment and threatens the safety of life and property. Therefore, accurate prediction of blast-induced ground vibration (BIGV) is a prerequisite for safe construction. In view of the fact that traditional blasting peak particle velocity (PPV) empirical formulas cannot be accurately predicted, this study selected 88 sets of blasting monitoring data, based on distance from the blast-face, maximum charge per delay, total charge, hole depth, spacing, burden, stemming length, and powder factor being used as input variables and PPV being used as output variable to characterize BIGV. First, a nonlinear mapping relationship between input variables and output variable is established through the Gaussian process (GP). The differential evolution algorithm (DE) is used to optimize the hyperparameters σf, σn, and l of the GP, and a blasting PPV model based on the DE-GP is constructed. The proposed model is compared with the empirical formulas, least square support vector machine (LSSVM), artificial neural network (ANN), and GP model, and its prediction performance is evaluated by statistical indicators such as root mean square error (RMSE). Finally, the cosine amplitude method (CAM) is used to analyze the sensitivity of blasting parameters. The results show that the DE-GP algorithm for blasting vibration velocity prediction has higher precision and accuracy, which is significantly better than other models, and is the closest to the measured PPV. Distance from the blast-face, total charge, and maximum charge per delay have a greater impact on the prediction of PPV, while stemming length and powder factor have a smaller impact on the prediction of PPV. The DE-GP model proposed by this research has certain reference value for the prediction and control of PPV in blasting construction.

Landslide Susceptibility Zonation With Special Emphasis on Tectonic Features for Occurrence of Landslides in Lower Indian Himalaya

Landslide susceptibility zonation (LSZ) map is prepared for a part of the lower Himalayas using the fuzzy cosine amplitude method. The prepared LSZ map is validated both statistically and physically, and it is found that around 30% of the study area lies in zones of high to very high landslide susceptibility. To correlate the Himalayan seismicity with the landslide hazard, the prepared LSZ map is further assessed in terms of the impact of fault distance on the occurrence and spatial distribution of landslides in the study area. The results quantify a strong inverse correlation between fault distance and the probability of landslide occurrence. A high coefficient of determination (R2 = 0.986) indicates that the equation can realistically predict the impact of fault distance on the study area's landslide occurrence. Further, landslide relative frequency (LRF) method is used to evaluate the statistical contribution of fault distance toward overall landslide susceptibility for the study area. It is found that distance from any major tectonic feature is a significant landslide causative parameter, with a relative importance of 21%. The prepared LSZ map will be valuable for decision-makers during smart town planning and sustainable development in the study area.

On the Performance Assessment of ANN and Spotted Hyena Optimized ANN to Predict the Spontaneous Combustion Liability of Coal

ABSTRACT Spontaneous combustion is the greatest hurdle faced in coal mining, requiring a more precise solution that is effective to predict the likelihood of its occurrence. The phenomenon has long been a concern affecting flora and fauna, mining machinery, coal reserves, and consuming essential and nonrenewable resources. To predict this occurrence, the application and performance evaluation of artificial neural network (ANN) and spotted hyena optimized ANN (SHO-ANN) were used to develop reliable models in predicting coal spontaneous combustion liability. The coefficient of determination (R2), mean error (ME), and root-mean-squared error (RMSE) were used to assess the performances of the models based on sixty-eight (68) datasets. The datasets were used for training, testing, and validation. The models provide excellent predicting ability with an R2 value close to 1, errors close to zero, while most of the data points fall within ± 3% error bands. Sensitivity analysis was conducted using the cosine amplitude method and the result shows volatile matter (VM) and Oxygen (O) as having the highest influence on the Wits-Ehac and FCC (Feng, Chakravorty, Cochrane) liability indices, while O and nitrogen (N) have the highest influence on crossing point temperature (XPT). In addition, this study provides a graphic user interface for the practical implementation of the proposed models in the coal mines.

Prediction of blast‑induced ground vibrations in quarry sites: a comparison of GP, RSM and MARS

Among the side effects caused by the blast, ground vibration (GV) is the most important one and can make serious damages to the surrounding structures. According to many scholars, the peak particle velocity (PPV) is one of the main indicators for determining the extent of blast‑induced GVs. Recently, following the rapid growth of soft computing approaches, researchers have tried to use these new techniques. This paper aims to explore three methods of soft computing including genetic programming (GP), response surface methodology (RSM), and multivariate adaptive regression splines (MARS) to predict the PPV values. For this purpose, a dataset of 200 published data including PPV, distance from the blasting face (D), and charge weight per delay (W) was used. The data have been recorded using blast seismograph, during the blast-induced earthquake triggered at 10 quarry sites in Ibadan and Abeokuta areas, Nigeria (https://doi.org/10.1016/j.dib.2018.04.103). The coefficient of determination for the MARS model as a most accurate model built in this research based on overall data results (R2 = 0.81), compared with the most accurate empirical equations presented in the research literature, namely general predictor model (R2 = 0.78), had a variation equal to 0.02. This variation for the root mean of squared error (RMSE), mean of absolute deviation (MAD), and mean of absolute percent error (MAPE) values were equal to 0.85, 0.25, and 0.38, respectively. In addition, the sensitivity analysis using cosine amplitude method (CAM) showed that the influence of each D and W parameters on PPV values based on developed models by this paper was more similar with the influence of these parameters based on the actual values, compared to empirical models. Finally, the parametric studies to investigate the behavior of various developed models were done to survey the changes to the values of the two variables D and W.

Strength Characteristics of Cement-Rice Husk Ash Stabilised Sand-Clay Mixture Reinforced with Polypropylene Fibers

In this paper, stress-strain behaviour of sand-clay mixture stabilised with different cement and rice husk ash percentages, and reinforced with different polypropylene fibre lengths are evaluated. Mixtures are widely used in road construction for soil stabilisation. It is observed that replacing half of the cement percentage (in high cement contents) with rice husk ash will result in a higher unconfined compressive strength. In addition, the presence of 6 mm polypropylene fibres will help to increase the unconfined compressive strength of stabilised samples, while larger fibres cause reverse behaviour. In addition, introducing a new index for assessing the effect of curing days. Curing Improvement Index it is obtained that larger fibres show higher Curing Improvement Index values. Results gained for the effects of curing days, and fibre lengths are further discussed and interpreted using Scanning Electron Microscopy photos. Based on the conducted Unconfined Compressive Strength, Indirect Tensile Strength, and Flexural Strength tests and using evolutionary polynomial regression modelling, some simple relations for prediction of unconfined compressive strength, indirect tensile strength, and flexural strength of cement-rice husk ash stabilised, and fibre reinforced samples are presented. High coefficients of determination of developed equations with experimental data show the accuracy of proposed relationships. Moreover, using a sensitivity analysis based on Cosine Amplitude Method, cement percentage and the length of polypropylene fibres used to reinforce the stabilised samples are respectively reported as the most and the least effective parameters on the unconfined compressive strength of specimens.

Constrained Compression Models for Tire-Derived Aggregate-Sand Mixtures Using Enhanced Large Scale Oedometer Testing Apparatus

Tire derived aggregates have recently been in wide use both in industry and engineering applications depending on the size and the application sought. Five different contents of tire derived aggregates (TDA) were mixed with sand thoroughly to ensure homogeneity. A series of large scale oedometer experiments were conducted to investigate the compressibility properties of the mixtures. Tire shreds content, TDA aspect ratio, skeletal relative density and overburden pressure are studied parameters. Constrained deformation modulus and coefficient of earth pressure at rest are measured parameters. All tests were conducted at seven overburden pressure levels. It was concluded that deformability of TDA-sand mixture increases with soft inclusion. Overburden pressure and skeletal relative density are also important parameters which render more rigidity and less lateral earth pressure coefficient accordingly. TDA size or aspect ratio was shown to have minor effect at least for the constrained strain conditions encountered in current study. An EPR-based parametric study and also sensitivity analyses based on cosine amplitude method revealed quantitative evaluation of the relative importance of each input parameter in varying deformation and lateral earth pressure coefficient as the outputs.

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACOR), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R2, root mean square error, RMSE (m3/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.

Control of Ground Settlements Caused by EPBS Tunneling Using an Intelligent Predictive Model

This paper deals with the control of ground surface settlement due to excavation of shallow tunnels. In order to control the settlement, one should be able to predict it, based on the prediction one may consider required preventions and protections. Prediction of surface settlement depends on several parameters and each parameter has an effect on the other. Application of the traditional methods could become impractical as the proposed equations might have low accuracy. To overcome these limitations, intelligent methods could be implemented. The present study aims to develop an intelligent model for prediction of the surface settlement in Shanghai subway line 2 project using adaptive neuro-fuzzy inference system (ANFIS). The results indicated that the proposed model had an appropriate performance. In order to perform sensitivity analysis of the ANFIS model, cosine amplitude method (CAM) was used and according to the results it was found that the operational, geometric and strength parameters had the highest impacts, respectively. Furthermore, amongst the input parameters, the two parameters of grout filling percentage (n) and grouting pressure (P) were identified as the most effective ones. The values of critical settlement were determined based on Rankin’s criteria of damage risk assessment to control the ground settlement. Then, the corresponding surface settlement was minimized by changing values of the input parameters. According to the results, control of machine operational factors particularly the n and P parameters had a crucial role in reducing surface settlement and preventing pertinent damages.

Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

Coupled Piled Raft Foundations (CPRFs) are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

Calculation of maximum surface settlement induced by EPB shield tunnelling and introducing most effective parameter

This study aims to predict ground surface settlement due to shallow tunneling and introduce the most affecting parameters on this phenomenon. Based on data collected from Shanghai LRT Line 2 project undertaken by TBM-EPB method, this research has considered the tunnel’s geometric, strength, and operational factors as the dependent variables. At first, multiple regression (MR) method was used to propose equations based on various parameters. The results indicated the dependency of surface settlement on many parameters so that the interactions among different parameters make it impossible to use MR method as it leads to equations of poor accuracy. As such, adaptive neuro-fuzzy inference system (ANFIS), was used to evaluate its capabilities in terms of predicting surface settlement. Among generated ANFIS models, the model with all input parameters considered produced the best prediction, so as its associated R2 in the test phase was obtained to be 0.957. The equations and models in which operational factors were taken into consideration gave better prediction results indicating larger relative effect of such factors. For sensitivity analysis of ANFIS model, cosine amplitude method (CAM) was employed; among other dependent variables, fill factor of grouting (n) and grouting pressure (P) were identified as the most affecting parameters.

Model-based sensitivity analysis of machining-induced residual stress under minimum quantity lubrication

This article presents a sensitivity analysis of residual stress based on the verified residual stress prediction model. The machining-induced residual stress is developed as a function of cutting parameters, tool geometry, material properties, and lubrication conditions. Based on the residual stress predictive model, the main effects of the cutting force, cutting temperature, and residual stress are quantitatively analyzed through the cosine amplitude method. The parametric study is carried out to investigate the effects of minimum quantity lubrication parameters, cutting parameters, and tool geometry on the cutting performances. Results manifest that the cutting force and residual stress are more sensitive to the heat transfer coefficient and the depth of cut, while the cutting temperature is more sensitive to the cutting speed. Large maximum compressive residual stress is obtained under a lower flow rate of minimum quantity lubrication, small depth of cut, and the proper air–oil mixture ratio. This research can support the controlling and optimization of residual stress in industrial engineering by strategically adjusting the application parameters of minimum quantity lubrication.