Conventional Empirical Method Research Articles

PREDICTING GEOTECHNICAL AXIAL CAPACITY OF REINFORCED CONCRETE DRIVEN PILE USING MACHINE LEARNING TECHNIQUE

Modified Meyerhof method is a popular method to calculate pile geotechnical axial capacity in Malaysia currently. From past experience, pile design based on empirical and analytical method produce variability of predicted capacity, in which, there is a wide scatter of predicted capacities and tendency for the predictions to be conservative, i.e. to underestimate the load capacity. This study provides options of machine learning and statistical approach for prediction of pile capacity based on soil investigation and dynamic pile load test result. It serves as an additional checking for engineer during design of pile based on conventional empirical method. It also helps to provide deeper insights of non-linear variables related to pile capacity through machine learning and statistical approach. This study helps engineer to design pile foundation optimally, economically and safely. The prediction of pile geotechnical axial capacity with machine learning technique and statistical approach for local marine clay soil in Penang, Malaysia is proposed in this study. The information from soil investigation report and dynamic pile load test report are gathered from six projects at Batu Kawan and Nibong Tebal located in Penang state that contributed 439 numbers of data. The skin friction factor, end bearing factor and pile geotechnical axial capacity are computed and predicted using empirical method, machine learning model and statistical model. Support Vector Machine illustrate best fit model for predicting skin friction factor with R2 of 0.517 while Random Forest seems to be the best fit model for predicting end bearing factor with R2 of 0.264. Random Forest is found to be the best model in predicting the geotechnical pile axial capacity compare to other models as it explains 96.2% of the variability of pile capacity.

On unified characterization of wave velocity of sand: A focus on effect of particle shape

This technical note investigates the wave velocities of sand with different particle shape. The grain shape was evaluated by three shape descriptors, including the sphericity (S), aspect ratio (AR) and roundness (R). It was established that under similar conditions in terms of the packing density and the stress level, the shear and compression wave velocities decrease with the particle roundness. This trend can be predicted using the conventional empirical method. Yet, it needs a significant number of fitting parameters, thus hindering the application of the conventional method in the real problems. To address this concern, a better method was proposed to estimate the wave velocities by introducing the state parameter in the framework of critical state soil mechanics. It is shown that the new method allows a unified characterization of wave velocity in sand, while the combinations of shape descriptors are no longer used in the prediction. The predictive performance of the proposed method was further confirmed by the experiment results of sand with rotund particle shape from the literature.

Estimation of in-situ rock strength from borehole geophysical logs in Australian coal mine sites

This study aims to improve the conventional empirical rock strength estimation method that is widely adopted in the Australian coal mining industry and provide more accurate rock uniaxial compression strength (UCS) predictions based on artificial neural network (ANN) models. 274 laboratory-measured UCS data (sample dimension: 60 mm (diameter) × 150 mm (height)) were collected from two longwall coal mines in Australia, along with the four geophysical logs (sonic, gamma, neutron, and porosity logs) and rock density of the tested core specimens. A two-layer ANN model was developed from these data based on the Levenberg–Marquardt algorithm as a base model. As compared to conventional sonic-UCS fitting equations, the mean percentage error, root mean squared error and maximum absolute error of the proposed ANN model were reduced from 34.27%, 15.23 MPa and 70.66 MPa to 20.67%, 11.02 MPa and 47.66 MPa, respectively. Further investigations were conducted by splitting the dataset based on rock lithology and mine locations, and separate ANN models were developed based on the divided subsets. Both lithology-specific and site-specific models exhibited improved estimation accuracy in comparison to the base model, indicating lithology and local geological conditions could considerably affect the estimation accuracy. Among the three types of models, the lithology-specific models yielded the most accurate predictions over the 274 data (MPE: 17.55%; RMSE: 8.84 MPa), followed by the site-specific models and the base model in descending order. Overall, all three models significantly outperformed the current empirical methods adopted in the studied mine sites. The outcomes of this study could provide more accurate UCS predictions for further geotechnical analysis in underground constructions, such as tunnel stability analysis and estimating in-situ stresses based on borehole breakout.

Optimal position and apex-subchondral bone distance of cannulated screws for internal fixation of femoral neck fracture: A radiological study.

To determine the optimal position of three cannulated screws in an inverted triangle pattern for internal fixation of femoral neck fractures, including the apex-subchondral bone distance, computed tomography (CT) images of the hip were processed using the MIMICS (Materialize Interactive Medical Image Control System) Research software. Points adjacent to the inferior, anterosuperior, and posterior cortex of the femoral neck were selected on the axial view. Subsequently, the positions of these points were adjusted to maximize the area between them, and splines were drawn in the coronal and sagittal planes to represent the cannulated screws. Finally, the virtual anteroposterior and lateral fluoroscopy images of the proximal femur and splines were created and the parameters calculated. Finite element analysis showed this fixation scheme provides stronger fracture support and stability. Twenty patients with femoral neck fractures (Modified group), with the area and perimeter of the triangle formed on axial postoperative CT images, including the shortest distance from the tip of the screw to the subchondral bone was compared with 23 matched patients for whom the conventional inverted isosceles triangle configuration of screws for internal fixation was used (Conventional group). The area and perimeter of the stabilization screws were larger, with a shorter apex-subchondral bone distance for the Modified group. There was no incidence of screw penetration in the femoral neck or head in either group, and all fractures healed within a follow-up period of 10 months. The modified screw placement method is simple and safe and provides greater fracture stability than the conventional empirical method of fixation.

Rock joint roughness determination method based on deep learning of time–frequency​ spectrogram

Aiming at the problem that it is hard to fully reflect the surface morphology of rock joints with a certain feature parameter, a joint roughness determination method based on deep learning of time–frequency spectrogram was proposed. Firstly, regarding the rock joint profile as a time series signal, the time–frequency spectrogram was drawn through the Short-Time Fourier Transform (STFT) to comprehensively characterize the undulation location and degree of joint, and describe the roughness feature information of the joint profile more clearly and accurately. Then, the deep convolutional neural network was combined to extract and learn the features of the time–frequency spectrogram, and the roughness coefficient of the rock joint profile was identified, which effectively recovered the deficiency of the traditional artificial formulation feature parameters. The experimental results showed that compared with the conventional empirical regression method and machine learning (ML) models, the identified joint roughness coefficient based on deep learning of the time–frequency spectrogram was more consistent with the true value, and the calculation results were more reasonable and reliable, with higher recognition accuracy and generalization ability. It verified the feasibility and effectiveness of deep learning technology in the field of rock joint roughness identification. Finally, the influence of the sampling interval in this method was analyzed, and it suggested that the sampling interval of this method should not be longer than 0.4 mm when evaluating the joint roughness with about 10 cm in length.

Integrating remotely sensed water stress factor with a crop growth model for winter wheat yield estimation in the North China Plain during 2008–2018

Accurate estimation of regional-scale crop yield under drought conditions allows farmers and agricultural agencies to make well-informed decisions and guide agronomic management. However, few studies have focused on using the crop model data assimilation (CMDA) method for regional-scale winter wheat yield estimation under drought stress and partial-irrigation conditions. In this study, we developed a CMDA framework to integrate remotely sensed water stress factor (MOD16 ET PET−1) with the WOFOST model using an ensemble Kalman filter (EnKF) for winter wheat yield estimation at the regional scale in the North China Plain (NCP) during 2008–2018. According to our results, integration of MOD16 ET PET−1 with the WOFOST model produced more accurate estimates of regional winter wheat yield than open-loop simulation. The correlation coefficient of simulated yield with statistical yield increased for each year and error decreased in most years, with r ranging from 0.28 to 0.65 and RMSE ranging from 700.08 to 1966.12 kg ha−1. Yield estimation using the CMDA method was more suitable in drought years (r = 0.47, RMSE = 919.04 kg ha−1) than in normal years (r = 0.30, RMSE = 1215.51 kg ha−1). Our approach performed better in yield estimation under drought conditions than the conventional empirical correlation method using vegetation condition index (VCI). This research highlighted the potential of assimilating remotely sensed water stress factor, which can account for irrigation benefit, into crop model for improving the accuracy of winter wheat yield estimation at the regional scale especially under drought conditions, and this approach can be easily adapted to other regions and crops.

Extraction method of weak underwater acoustic signal based on the combination of wavelet transform and empirical mode decomposition

When studying underwater acoustic exploration, tracking and positioning, the target signals collected by hydrophones are often submerged in strong intermittent noise and environmental noise. In this paper, an algorithm that combines empirical mode decomposition and wavelet transform is proposed to achieve the efficient extraction of target signals in the environment with strong noise. First the calibration of baseline drift is performed on the algorithm, and then it is decomposed into different intrinsic mode functions via empirical mode. The wavelet threshold processing is conducted according to the correlation coefficient of each mode component and the original signal, and finally the signals are reconstructed. The simulation and experiment results show that compared with the conventional empirical mode decomposition method and wavelet threshold method, when the signal-to-noise ratio is low and there exist high-frequency intermittent jamming and baseline drift, the combined algorithm can better extract the target signal, laying the foundation for direction-of-arrival estimation and target positioning in the next step.

Exploring Artificial Neural Network to Evaluate the Undrained Shear Strength of Soil from Cone Penetration Test Data

In geotechnical design practices, the undrained shear strength of soil is regarded as one of the engineering properties of paramount importance. Over the past years, several theoretical and empirical methods have been developed to estimate the undrained shear strength based on soil properties using in-situ tests such as cone and piezocone penetration tests (CPT and PCPT). However, most of these methods involve correlation assumptions that can result in inconsistent accuracy. In this paper, an artificial neural network (ANN) is used to devise a model with a better and more consistent prediction of the undrained shear strength of soil from CPT data. The ANN algorithm does not require such assumptions as it learns from previous cases/instances. A database was prepared of soil boring data and laboratory test data along with corresponding CPT/PCPT data from 70 test sites located in 14 different parishes in Louisiana. Presenting this data to the ANN, models were trained through trial and error using different network algorithms, such as back propagation method and quasi-Newton method. Different ANN models were trained using corrected cone tip resistance and sleeve friction input data along with some other easily measurable soil properties. The results of ANN models were then compared with a conventional empirical method of determining undrained shear strength of soil from CPT parameters. The results of this study clearly demonstrated that the ANN models outperformed the conventional empirical method, which confirms the applicability of ANN in the evaluation of the undrained shear strength of soil from CPT data.

Estimation of the average oxidation number of nickel in a nickel oxide based on local structural information

We developed the methods to estimate the average oxidation number of Ni in a nickel oxide from the local structural information on the first nearest neighbor Ni–O shell obtained by extended X-ray absorption fine structure curve fitting analysis. The estimation method utilizes the Jahn-Teller distortion around Ni3+ sites, enabling the evaluation of local structural parameters and mole fractions for different Ni valence states. The average oxidation numbers of Ni in nickel oxides estimated by the methods proposed in this work exhibit good agreement with those reported or evaluated by a conventional method using instrumental and solution chemical analyses. It is demonstrated that the estimation methods also work well in an in situ X-ray absorption spectroscopy (XAS) study on the charge-compensating behavior of Ni in a Ni-containing battery electrode material. The estimation methods are based on Ni K-edge XAS using highly penetrating hard X-ray, and thus applicable to in situ XAS studies. The methods proposed in this work are expected to work as substitutes for the conventional empirical method using X-ray absorption near edge structure. The estimation methods would be important choices when the local environment around Ni could dramatically be changed during charge/discharge cycles, like in an overcharge condition.

S-wave velocity prediction of marine-continental transitional coal measure rocks using multipole array sonic logs and the rock-matrix modulus extraction method

Accurate prediction of the S-wave velocity of highly heterogeneous coal measure strata using high-resolution logging can effectively identify high-quality reservoirs. We have used multipole array sonic logs to predict the S-wave velocity of coal measure strata based on the conventional empirical method (CEM), multiple regression method (MRM), and rock-matrix modulus extraction (MME) method. Moreover, we used a complex multiple parameter iterative computational method of forward calculation and inversion in the MME method. Our results indicate that the MME method can effectively extract several rock modulus parameters. There are good binomial relationships between the extracted rock modulus parameters ([Formula: see text]) and between the extracted modulus parameters and the P-wave impedance ([Formula: see text]). The average relative errors of the S-wave velocities predicted by the CEM, MRM, and MME methods are 7.58%, 5.64%, and 2.31%, respectively. The MME method can effectively extract and couple effective information from different types of conventional well logs and perform high-precision S-wave time difference prediction.

Experimental Study of Laminar Cooling Process on Temperature Field of the Heavy Plate

Laminar cooling is the key to hot rolling and controlled cooling. It can improve the performance of steel by changing the grain transformation. The key to improve product quality is that the laminar cooling process model can calculate the cooling strategy accurately. The conventional empirical formula calculation method can’t guarantee the accuracy of the water cooling heat transfer coefficient, which is the critical parameters of laminar cooling process. Therefore, an experimental study like this is needed. In this paper, the temperature measurement experiment of laminar cooling is carried out by the method of experimental temperature measurement. The experimental design stage uses COMSOL software to simulate the temperature field temperature to determine the diameter and depth of the measurement. Then the laminar cooling experiment is carried out and collects the temperature measurement data under different conditions. The error of the measured temperature field is less than 0.1% under the final experimental conditions. The heat transfer coefficient is obtained by experimental data and input in heat transfer model, the surface temperature is calculated, and the temperature is compared with the experimental data, the relative error is less than 20°C.

Enhanced Biomass Characteristics Index in palm biomass calorific value estimation

Oil palm industry contributes a huge amount of valuable crude palm oil, and simultaneously producing a large quantity of plantation waste or biomass, which will be utilized as fuel. In order to give a clear insight of the energy output estimation from the biomass, a comprehensive study on the physical properties of the biomass: bulk density and moisture content is crucial. In a conventional approach, these properties are obtained through empirical methods on individual sample basis. However, the conventional empirical methods have several drawbacks: (i) require a huge amount of experimental results to construct biomass properties’ curve (ii) data variation affects the accuracy of analysis result. These create a limitation in properties estimation and further affecting the optimum biomass utilization. To tackle this issue, there is a need to search for a direct representation of the properties. A Biomass Characteristics Index (BCI) is proposed to represent the relationship between bulk density and moisture content. A numerical framework is developed to determine the BCI. This index is used to estimate the biomass bulk density and moisture content before the calorific value calculation. A regression graph is plotted to illustrate the relationship among those values with respect to different appearance shapes of biomass. The result shows that different size and shape of biomass has its own specific BCI. The classification of biomass according to its specific BCI can forecast the related bulk density and moisture content. Therefore, it reduces the hassle, especially in terms of time constraint to get those values through conventional empirical method. This will increase the overall biomass operational management efficiency.

Biomass Characteristics Index: a Numerical Approach in Palm Bio-energy Estimation

Oil palm industry contributes a huge amount of valuable crude palm oil, also directly produces large quantity of plantation waste or biomass if it will be utilized as a fuel. In order to give a clear insight of the energy output estimation from the biomass, a comprehensive study on the physical properties of the biomass: bulk density and moisture content is crucial. In conventional approach, these properties are obtained through empirical methods on individual sample basis. There are several drawbacks from the conventional empirical methods: (i) need a huge amount of experimental results to construct biomass properties’ curve (ii) data variation affects the accuracy of analysis result. These create a limitation in properties estimation and then further affect the optimum biomass utilization. To tackle this issue, there is a need to search for a direct representation of the properties. A Biomass Characteristics Index (BCI) is proposed to represent the relationship between bulk density and moisture content. A numerical framework is developed to determine the BCI. This index is used to estimate the biomass bulk density and moisture content before the calorific value calculation. A regression graph is plotted to illustrate the relationship among those values with respect to different appearance shapes of biomass. The result shows that different size and shape of biomass has its own specific BCI. The classification of biomass according to its specific BCI can forecast the related bulk density and moisture content. Therefore, it reduces the hassle and time constraint to get those values through conventional empirical method. This will increase the overall biomass operational management efficiency.

Boundary layer approach in the modeling of breaking solitary wave runup

The boundary layer is very important in the relation between wave motion and bed stress, such as sediment transport. It is a known fact that bed stress behavior is highly influenced by the boundary layer beneath the waves. Specifically, the boundary layer underneath wave runup is difficult to assess and thus, it has not yet been widely discussed, although its importance is significant. In this study, the shallow water equation (SWE) prediction of wave motion is improved by being coupled with the k–ω model, as opposed to the conventional empirical method, to approximate bed stress. Subsequently, the First Order Center Scheme and Monotonic Upstream Scheme of Conservation Laws (FORCE MUSCL), which is a finite volume shock-capturing scheme, is applied to extend the SWE range for breaking wave simulation. The proposed simultaneous coupling method (SCM) assumes the depth-averaged velocity from the SWE is equivalent to free stream velocity. In turn, free stream velocity is used to calculate a pressure gradient, which is then used by the k–ω model to approximate bed stress. Finally, this approximation is applied to the momentum equation in the SWE. Two experimental cases will be used to verify the SCM by comparing runup height, surface fluctuation, bed stress, and turbulent intensity values. The SCM shows good comparison to experimental data for all before-mentioned parameters. Further analysis shows that the wave Reynolds number increases as the wave propagates and that the turbulence behavior in the boundary layer gradually changes, such as the increase of turbulent intensity.

Analysis of ocean internal waves imaged by multichannel reflection seismics, using ensemble empirical mode decomposition

Research on ocean internal waves using seismic oceanography is a frontier issue both for marine geophysicists and physical oceanographers. Images of the ocean water layer obtained by conventional processing of multichannel seismic reflection data can show the overall patterns of internal waves. However, in order to extract more information from the seismic data, new tools need to be developed. Here, we use the ensemble empirical mode decomposition (EEMD) method to decompose vertical displacement data from seismic sections and apply this method to a seismic section from the northeastern South China Sea, where clear internal waves are observed. Compared with the conventional empirical mode decomposition method, EEMD has greatly reduced the scale mixing problems induced in the decomposition results. The results obtained show that the internal waves in this area are composed of different characteristic wavelengths at different depths. The depth range of 200–1050 m contains internal waves with a wavelength of 1.25 km that are very well coupled in the vertical direction. The internal waves with a wavelength of 3 km, in the depth range of 200–600 m, are also well coupled, but in an oblique direction; this suggests that the propagation speed of internal waves of this scale changes with depth in this area. Finally, the internal waves with a wavelength of 6.5 km, observed in the depth range of 200–800 m, are separated into two parts with a phase difference of about 90°, by a clear interface at a depth of 650 m; this allows us to infer an oblique propagation of wave energy of this scale.

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Saddlepoint condition on a predictor to reconfirm the need for the assumption of a prior distribution

Saddlepoint conditions on a predictor are introduced and developed to reconfirm the need for the assumption of a prior distribution in constructing a useful inferential procedure. A condition yields that the predictor induced from the maximum likelihood estimator is the worst under a loss, while the predictor induced from a suitable posterior mean is the best. This result indicates the promising role of Bayesian criteria, such as the deviance information criterion (DIC). As an implication, we critique the conventional empirical Bayes method because of its partial assumption of a prior distribution.

Design of an adaptive positioning system based on WiFi radio signals

With the remarkable advances in wireless networking and pervasive computing, there have been an increasing need to figure it into LBS (location-based services) applications. This paper proposes an adaptive positioning system for the mobile terminal (MT) localization using the radio propagation modeling (RPM) and Kalman filtering (KF) according to the measurements of signal-to-noise ratio (SNR) information between indoor IEEE 802.11b (WiFi) APs and reference points (Tags). Comparing with the conventional empirical method (such as the fingerprinting method), the adaptive RPM algorithm can perform on-line calibration in real dynamic environments and reduce the number of training points. Additionally, the KF algorithm is used to track the location of MTs, where the observation information is extracted from the empirical and RPM positioning methods. The numerical simulations and experimental results show that not only the calibrating and positioning method of the RPM algorithm can improve the accuracy of an indoor WiFi locating system and alleviate the phenomenon of aliasing in the signal space, but also the estimating and tracking method of the KF algorithm can overcome the problem of the aliasing and reduce the positioning error with smaller sampling time.

Improvement of the EMD method and applications in defect diagnosis of ball bearings

Based on the analysis of the empirical mode decomposition (EMD) method in this paper, an improved arithmetic for local mean is presented in detail. In the new arithmetic, the local mean is obtained by connecting all window means of the signals between two successive extrema with the cubic spline interpolation. The new arithmetic is more time saving than that used in the conventional empirical mode decomposition method. The improvement of the new arithmetic is validated by decomposing simulating signals. Moreover, the improved empirical mode decomposition method is successfully applied in decomposing vibration signals of ball bearings and provides a new method to diagnose defects of ball bearings. In contrast to the traditional envelope analysis, the empirical mode decomposition method could decompose the signal into different frequency bands adaptively, and the central frequency and bandwidth of band-pass filter, which was used to extract the resonance vibration, should no longer be decided. The results show that the EMD method is powerful for defect diagnosis of ball bearings.

A method of self-pursued boundary value on a body and the magnus effect calculated with this method

Abstract When the Navier-Stokes equations formulated in terms of stream function Ψ and vorticity ζ are numerically solved, we encounter the problem of determining the value of Ψ on a body in an external flow. Although this problem is especially important in cases with a shear flow, an unsteady flow, or a flow around a rotating circular cylinder, there are no efficient methods available which do not use empirical factors. The authors propose here a method of automatically determining the value of Ψ on a body with an arbitrary profile without using any empirical factors (named the SPB method) and apply this method to the case of a rotating circular cylinder in a uniform shear flow. A comparison of the results by the SPB method with those by the method of fixing the value of Ψ on a body and by the conventional empirical method showed that the SPB method is very efficient and is applicable to both steady and unsteady flows. Of course, the method always satisfies the condition of single valuedness of the pressure terms of the equation of motion. Through applying the SPB method to the case of a rotating circular cylinder in a uniform shear flow, the aerodynamic forces acting on the cylinder were obtained and the lift force was found to decrease as the velocity gradient of the shear flow increases when the rotational speed of the cylinder was kept constant.