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Liquefaction assessment using microtremor measurement, conventional method and artificial neural network

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

《结构与土木工程前沿（英文）》 2014年 第8卷 第3期   页码 292-307 doi: 10.1007/s11709-014-0256-8

摘要： Recent researchers have discovered microtremor applications for evaluating the liquefaction potential. Microtremor measurement is a fast, applicable and cost-effective method with extensive applications. In the present research the liquefaction potential has been reviewed by utilization of microtremor measurement results in Babol city. For this purpose microtremor measurements were performed at 60 measurement stations and the data were analyzed by suing Nakmaura’s method. By using the fundamental frequency and amplification factor, the value of vulnerability index ( ) was calculated and the liquefaction potential has been evaluated. To control the accuracy of this method, its output has been compared with the results of Seed and Idriss [ ] method in 30 excavated boreholes within the study area. Also, the results obtained by the artificial neural network (ANN) were compared with microtremor measurement. Regarding the results of these three methods, it was concluded that the threshold value of liquefaction potential is . On the basis of the analysis performed in this research it is concluded that microtremors have the capability of assessing the liquefaction potential with desirable accuracy.

关键词： liquefaction     microtremor     vulnerability index     artificial neural networks (ANN)     microzonation    

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QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

《结构与土木工程前沿（英文）》 2023年 第17卷 第1期   页码 25-36 doi: 10.1007/s11709-022-0908-z

摘要： In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

关键词： tunnel boring machine     control parameter optimization     quantum particle swarm optimization     artificial neural network     tunneling energy efficiency    

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An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties

Yaolin LIN, Wei YANG

《能源前沿（英文）》 2021年 第15卷 第2期   页码 550-563 doi: 10.1007/s11708-019-0607-1

摘要： With increasing awareness of sustainability, demands on optimized design of building shapes with a view to maximize its thermal performance have become stronger. Current research focuses more on building envelopes than shapes, and thermal comfort of building occupants has not been considered in maximizing thermal performance in building shape optimization. This paper attempts to develop an innovative ANN (artificial neural network)-exhaustive-listing method to optimize the building shapes and envelope physical properties in achieving maximum thermal performance as measured by both thermal load and comfort hour. After verified, the developed method is applied to four different building shapes in five different climate zones in China. It is found that the building shape needs to be treated separately to achieve sufficient accuracy of prediction of thermal performance and that the ANN is an accurate technique to develop models of discomfort hour with errors of less than 1.5%. It is also found that the optimal solutions favor the smallest window-to-external surface area with triple-layer low-E windows and insulation thickness of greater than 90 mm. The merit of the developed method is that it can rapidly reach the optimal solutions for most types of building shapes with more than two objective functions and large number of design variables.

关键词： ANN (artificial neural network)     exhaustive-listing     building shape     optimization     thermal load     thermal comfort    

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Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Simarjit KAUR, Yajvender Pal VERMA, Sunil AGRAWAL

《能源前沿（英文）》 2013年 第7卷 第4期   页码 468-478 doi: 10.1007/s11708-013-0282-6

摘要： In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.

关键词： artificial neural network (ANN)     frequency prediction     availability-based tariff (ABT)     generation scheduling (GS)    

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of spinal lumbar interbody fusion cage subsidence using Taguchi method, finite element analysis, and artificialneural network

Christopher John NASSAU, N. Scott LITOFSKY, Yuyi LIN

《机械工程前沿（英文）》 2012年 第7卷 第3期   页码 247-255 doi: 10.1007/s11465-012-0335-2

摘要：

Subsidence, when implant penetration induces failure of the vertebral body, occurs commonly after spinal reconstruction. Anterior lumbar interbody fusion (ALIF) cages may subside into the vertebral body and lead to kyphotic deformity. No previous studies have utilized an artificial neural network (ANN) for the design of a spinal interbody fusion cage. In this study, the neural network was applied after initiation from a Taguchi L18 orthogonal design array. Three-dimensional finite element analysis (FEA) was performed to address the resistance to subsidence based on the design changes of the material and cage contact region, including design of the ridges and size of the graft area. The calculated subsidence is derived from the ANN objective function which is defined as the resulting maximum von Mises stress (VMS) on the surface of a simulated bone body after axial compressive loading. The ANN was found to have minimized the bone surface VMS, thereby optimizing the ALIF cage given the design space. Therefore, the Taguchi-FEA-ANN approach can serve as an effective procedure for designing a spinal fusion cage and improving the biomechanical properties.

关键词： anterior lumbar interbody fusion (ALIF)     artificial neural network (ANN)     finite element     interbody cage     lumbar interbody fusion     subsidence     taguchi method    

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Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

《结构与土木工程前沿（英文）》 2014年 第8卷 第2期   页码 167-177 doi: 10.1007/s11709-014-0236-z

摘要： Bridge girders exposed to aggressive environmental conditions are subject to time-variant changes in resistance. There is therefore a need for evaluation procedures that produce accurate predictions of the load-carrying capacity and reliability of bridge structures to allow rational decisions to be made about repair, rehabilitation and expected life-cycle costs. This study deals with the stability of damaged steel I-beams with web opening subjected to bending loads. A three-dimensional (3D) finite element (FE) model using ABAQUS for the elastic flexural torsional analysis of I-beams has been used to assess the effect of web opening on the lateral buckling moment capacity. Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting the lateral-torsional buckling moment capacity of deteriorated steel I-beams with different sizes of rectangular web opening using obtained FE results. It is found out that the proposed formulae can accurately predict residual lateral buckling capacities of doubly-symmetric steel I-beams with rectangular web opening. Hence, the results of this study can be used for better prediction of buckling life of web opening of steel beams by practice engineers.

关键词： steel I-beams     lateral-torsional buckling     finite element (FE) method     artificial neural network (ANN) approach    

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Experimental investigation and ANN modeling on improved performance of an innovative method of using

Srinivasan CHANDRASEKARAN, Arunachalam AMARKARTHIK, Karuppan SIVAKUMAR, Dhanasekaran SELVAMUTHUKUMARAN, Shaji SIDNEY

《能源前沿（英文）》 2013年 第7卷 第3期   页码 279-287 doi: 10.1007/s11708-013-0268-4

摘要： To convert wave energy into usable forms of energy by utilizing heaving body, heaving bodies (buoys) which are buoyant in nature and float on the water surface are usually used. The wave exerts excess buoyancy force on the buoy, lifting it during the approach of wave crest while the gravity pulls it down during the wave trough. A hydraulic, direct or mechanical power takeoff is used to convert this up and down motion of the buoy to produce usable forms of energy. Though using a floating buoy for harnessing wave energy is conventional, this device faces many challenges in improving the overall conversion efficiency and survivability in extreme conditions. Up to the present, no studies have been done to harness ocean waves using a non-floating object and to find out the merits and demerits of the system. In the present paper, an innovative heaving body type of wave energy converter with a non-floating object was proposed to harness waves. It was also shown that the conversion efficiency and safety of the proposed device were significantly higher than any other device proposed with floating buoy. To demonstrate the improvements, experiments were conducted with non-floating body for different dimensions and the heave response was noted. Power generation was not considered in the experiment to observe the worst case response of the heaving body. The device was modeled in artificial neural network (ANN), the heave response for various parameters were predicted, and compared with the experimental results. It was found that the ANN model could predict the heave response with an accuracy of 99%.

关键词： ocean wave energy     point absorbers     heaving body     non-floating object     heave response ratio     artificial neural network (ANN)    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificialneural network

《结构与土木工程前沿（英文）》 2021年 第15卷 第5期   页码 1181-1198 doi: 10.1007/s11709-021-0744-6

摘要： In the recent era, piled raft foundation (PRF) has been considered an emergent technology for offshore and onshore structures. In previous studies, there is a lack of illustration regarding the load sharing and interaction behavior which are considered the main intents in the present study. Finite element (FE) models are prepared with various design variables in a double-layer soil system, and the load sharing and interaction factors of piled rafts are estimated. The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial neural network (ANN) modeling, and some prediction models are proposed. ANN models are prepared with Levenberg–Marquardt (LM) algorithm for load sharing and interaction factors through backpropagation technique. The factor of safety (FS) of PRF is also estimated using the proposed NMR and ANN models, which can be used for developing the design strategy of PRF.

关键词： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Prediction of bed load sediments using different artificial neural network models

Reza ASHEGHI, Seyed Abbas HOSSEINI

《结构与土木工程前沿（英文）》 2020年 第14卷 第2期   页码 374-386 doi: 10.1007/s11709-019-0600-0

摘要： Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.

关键词： bed load prediction     artificial neural network     modeling     empirical equations    

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基于RBF神经网络算法的连拱隧道围岩变形预测方法研究

肖智旺,钟登华

《中国工程科学》 2008年 第10卷 第7期   页码 77-81

摘要：

利用径向基函数前馈式神经网络的特性，构建了连拱隧洞围岩变形的预测模型，并利用Matlab工具对模型进行求解。最后的工程实例对文章的方法进行了检验，其结果表明，此方法具有求解速度快，结果更为优化、预测效果更好等优点。

关键词： 连拱隧洞     围岩变形     变形预测     径向基函数(RBF)     神经网络    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

《结构与土木工程前沿（英文）》 2013年 第7卷 第2期   页码 133-136 doi: 10.1007/s11709-013-0202-1

摘要： This article examines the capability of Gaussian process regression (GPR) for prediction of effective stress parameter ( ) of unsaturated soil. GPR method proceeds by parameterising a covariance function, and then infers the parameters given the data set. Input variables of GPR are net confining pressure ( ), saturated volumetric water content ( ), residual water content ( ), bubbling pressure ( ), suction ( ) and fitting parameter ( ). A comparative study has been carried out between the developed GPR and Artificial Neural Network (ANN) models. A sensitivity analysis has been done to determine the effect of each input parameter on . The developed GPR gives the variance of predicted . The results show that the developed GPR is reliable model for prediction of of unsaturated soil.

关键词： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network (ANN)     variance    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

《结构与土木工程前沿（英文）》 2017年 第11卷 第1期   页码 90-99 doi: 10.1007/s11709-016-0363-9

摘要： Evaluating the in situ concrete compressive strength by means of cores cut from hardened concrete is acknowledged as the most ordinary method, however, it is very difficult to predict the compressive strength of concrete since it is affected by many factors such as different mix designs, methods of mixing, curing conditions, compaction, etc. In this paper, considering the experimental results, three different models of multiple linear regression model (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) are established, trained, and tested within the Matlab programming environment for predicting the 28 days compressive strength of concrete with 173 different mix designs. Finally, these three models are compared with each other and resulted in the fact that ANN and ANFIS models enables us to reliably evaluate the compressive strength of concrete with different mix designs, however, multiple linear regression model is not feasible enough in this area because of nonlinear relationship between the concrete mix parameters. Finally, the sensitivity analysis (SA) for two different sets of parameters on the concrete compressive strength prediction are carried out.

关键词： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity analysis (SA)    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

《结构与土木工程前沿（英文）》 2019年 第13卷 第1期   页码 215-239 doi: 10.1007/s11709-018-0489-z

摘要： Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.

关键词： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine     parametric analysis    

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Real-time tool condition monitoring method based on temperature measurement and artificial neural network

《机械工程前沿（英文）》 doi: 10.1007/s11465-021-0661-3

摘要： Tool failures in machining processes often cause severe damages of workpieces and lead to large quantities of loss, making tool condition monitoring an important, urgent issue. However, problems such as practicability still remain in actual machining. Here, a real-time tool condition monitoring method integrated in an in situ fiber optic temperature measuring apparatus is proposed. A thermal simulation is conducted to investigate how the fluctuating cutting heats affect the measuring temperatures, and an intermittent cutting experiment is carried out, verifying that the apparatus can capture the rapid but slight temperature undulations. Fourier transform is carried out. The spectrum features are then selected and input into the artificial neural network for classification, and a caution is given if the tool is worn. A learning rate adaption algorithm is introduced, greatly reducing the dependence on initial parameters, making training convenient and flexible. The accuracy stays 90% and higher in variable argument processes. Furthermore, an application program with a graphical user interface is constructed to present real-time results, confirming the practicality.

关键词： tool condition monitoring     cutting temperature     neural network     learning rate adaption    

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Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY, A. SATHYABHAMA, P. Srinivasa PAI

《能源前沿（英文）》 2020年 第14卷 第2期   页码 347-358 doi: 10.1007/s11708-018-0553-3

摘要： Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

关键词： power curve     method of least squares     cubic spline interpolation     response surface methodology     artificial neural network (ANN)    

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标题 作者 时间 类型 操作

Liquefaction assessment using microtremor measurement, conventional method and artificial neural network

Sadegh REZAEI,Asskar Janalizadeh CHOOBBASTI

期刊论文

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

期刊论文

An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties

Yaolin LIN, Wei YANG

期刊论文

Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment

Simarjit KAUR, Yajvender Pal VERMA, Sunil AGRAWAL

期刊论文

of spinal lumbar interbody fusion cage subsidence using Taguchi method, finite element analysis, and artificialneural network

Christopher John NASSAU, N. Scott LITOFSKY, Yuyi LIN

期刊论文

Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

期刊论文

Experimental investigation and ANN modeling on improved performance of an innovative method of using

Srinivasan CHANDRASEKARAN, Arunachalam AMARKARTHIK, Karuppan SIVAKUMAR, Dhanasekaran SELVAMUTHUKUMARAN, Shaji SIDNEY

期刊论文

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based artificialneural network

期刊论文

Prediction of bed load sediments using different artificial neural network models

Reza ASHEGHI, Seyed Abbas HOSSEINI

期刊论文

基于RBF神经网络算法的连拱隧道围岩变形预测方法研究

肖智旺,钟登华

期刊论文

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

期刊论文

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

期刊论文

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

期刊论文

Real-time tool condition monitoring method based on temperature measurement and artificial neural network

期刊论文

Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY, A. SATHYABHAMA, P. Srinivasa PAI

期刊论文