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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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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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combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificialneural network

Arunachalam VELMURUGAN,Marimuthu LOGANATHAN,E. James GUNASEKARAN

《能源前沿（英文）》 2016年 第10卷 第1期   页码 114-124 doi: 10.1007/s11708-016-0394-x

摘要： This paper explores the use of artificial neural networks (ANN) to predict performance, combustion and emissions of a single cylinder, four stroke stationary, diesel engine operated by thermal cracked cashew nut shell liquid (TC-CNSL) as the biodiesel blended with diesel. The tests were performed at three different injection timings (21°, 23°, 25°CA bTDC) by changing the thickness of the advance shim. The ANN was used to predict eight different engine-output responses, namely brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), carbon monoxide (CO), oxide of nitrogen (NO ), hydrocarbon (HC), maximum pressure ( ) and heat release rate (HRR). Four pertinent engine operating parameters, i.e., injection timing (IT), injection pressure (IP), blend percentage and pecentage load were used as the input parameters for this modeling work. The ANN results show that there is a good correlation between the ANN predicted values and the experimental values for various engine performances, combustion parameters and exhaust emission characteristics. The mean square error value (MSE) is 0.005621 and the regression value of is 0.99316 for training, 0.98812 for validation, 0.9841 for testing while the overall value is 0.99173. Thus the developed ANN model is fairly powerful for predicting the performance, combustion and exhaust emissions of internal combustion engines.

关键词： cashew nut shell liquid (CNSL)     artificial neural networks (ANN)     thermal cracking     mean square error (MSE)    

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基于GA-ANN的震灾风险预测模型研究

刘明广,郭章林

《中国工程科学》 2006年 第8卷 第3期   页码 83-86

摘要：

对震灾的各种主要风险因素进行系统的辨识和分析，并建立了震灾风险预测的遗传神经网络模型，用实例证明了该模型的可行性与有效性，为决策部门提供一种有效的震灾风险预测方法。

关键词： 地震灾害     风险因素     人工神经网络     遗传算法     预测    

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Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

《结构与土木工程前沿（英文）》 2018年 第12卷 第4期   页码 490-503 doi: 10.1007/s11709-017-0445-3

摘要： This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of SIFCON containing different minerals admixture. The investigations were done on 84 SIFCON mixes, and specimens were cast and tested after 28 days curing. The obtained experimental data are trained using ANN which consists of 4 input parameters like Percentage of fiber (PF), Aspect Ratio (AR), Type of admixture (TA) and Percentage of admixture (PA). The corresponding output parameters are compressive strength, tensile strength and flexural strength. The predicted values obtained using ANN show a good correlation between the experimental data. The performance of the 4-14-3 architecture was better than other architectures. It is concluded that ANN is a highly powerful tool suitable for assessing the strength characteristics of SIFCON.

关键词： artificial neural networks     root mean square error     SIFCON     silica fume     metakaolin     steel fiber    

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

《化学科学与工程前沿（英文）》 2013年 第7卷 第3期   页码 357-365 doi: 10.1007/s11705-013-1336-3

摘要： Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO (SC-CO ) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient of determination ( ) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an of 0.9948.

关键词： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

《结构与土木工程前沿（英文）》 2020年 第14卷 第3期   页码 609-622 doi: 10.1007/s11709-020-0623-6

摘要： This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

关键词： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 520-536 doi: 10.1007/s11709-021-0689-9

摘要： This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

关键词： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificialneural networks

《环境科学与工程前沿（英文）》 2023年 第17卷 第1期 doi: 10.1007/s11783-023-1606-3

摘要：

● Reducting the sampling frequency can enhance the modelling process.

关键词： HDPE     Pyrolysis     Kinetics     Thermogravimetric     ANOVA     Artificial neural network    

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Service life prediction of fly ash concrete using an artificial neural network

《结构与土木工程前沿（英文）》 2021年 第15卷 第3期   页码 793-805 doi: 10.1007/s11709-021-0717-9

摘要： Carbonation is one of the most aggressive phenomena affecting reinforced concrete structures and causing their degradation over time. Once reinforcement is altered by carbonation, the structure will no longer fulfill service requirements. For this purpose, the present work estimates the lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neural network technique. A collection of 300 data points was made from experimental results available in the published literature. Backpropagation training of a three-layer perceptron was selected for the calculation of weights and biases of the network to reach the desired performance. Six parameters affecting carbonation were used as input neurons: binder content, fly ash substitution rate, water/binder ratio, CO2 concentration, relative humidity, and concrete age. Moreover, experimental validation carried out for the developed model shows that the artificial neural network has strong potential as a feasible tool to accurately predict the carbonation depth of fly ash concrete. Finally, a mathematical formula is proposed that can be used to successfully estimate the service life of fly ash concrete.

关键词： concrete     fly ash     carbonation     neural networks     experimental validation     service life    

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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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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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Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificialneural network

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 976-989 doi: 10.1007/s11709-022-0840-2

摘要： Vibration-based damage detection methods have become widely used because of their advantages over traditional methods. This paper presents a new approach to identify the crack depth in steel beam structures based on vibration analysis using the Finite Element Method (FEM) and Artificial Neural Network (ANN) combined with Butterfly Optimization Algorithm (BOA). ANN is quite successful in such identification issues, but it has some limitations, such as reduction of error after system training is complete, which means the output does not provide optimal results. This paper improves ANN training after introducing BOA as a hybrid model (BOA-ANN). Natural frequencies are used as input parameters and crack depth as output. The data are collected from improved FEM using simulation tools (ABAQUS) based on different crack depths and locations as the first stage. Next, data are collected from experimental analysis of cracked beams based on different crack depths and locations to test the reliability of the presented technique. The proposed approach, compared to other methods, can predict crack depth with improved accuracy.

关键词： damage prediction     ANN     BOA     FEM     experimental modal analysis    

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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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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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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

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

Yasser SHARIFI,Sajjad TOHIDI

期刊论文

combustion and emission characteristics of diesel-thermal cracked cashew nut shell liquid blends using artificialneural network

Arunachalam VELMURUGAN,Marimuthu LOGANATHAN,E. James GUNASEKARAN

期刊论文

基于GA-ANN的震灾风险预测模型研究

刘明广,郭章林

期刊论文

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

期刊论文

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

期刊论文

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

期刊论文

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

期刊论文

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificialneural networks

期刊论文

Service life prediction of fly ash concrete using an artificial neural network

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

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

Yaolin LIN, Wei YANG

期刊论文

Vibration-based crack prediction on a beam model using hybrid butterfly optimization algorithm with artificialneural network

Abdelwahhab KHATIR; Roberto CAPOZUCCA; Samir KHATIR; Erica MAGAGNINI

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

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

期刊论文