Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

In this study, the least square support vector machine(LSSVM) algorithm was applied to predicting the bearing capacity of bored piles embedded in sand and mixed soils. Pile geometry and cone penetration test(CPT) results were used as input variables for prediction of pile bearing capacity. The data used were collected from the existing literature and consisted of 50 case records. The application of LSSVM was carried out by dividing the data into three sets: a training set for learning the problem and obtaining a relationship between input variables and pile bearing capacity, and testing and validation sets for evaluation of the predictive and generalization ability of the obtained relationship. The predictions of pile bearing capacity by LSSVM were evaluated by comparing with experimental data and with those by traditional CPT-based methods and the gene expression programming(GEP) model. It was found that the LSSVM performs well with coefficient of determination, mean, and standard deviation equivalent to 0.99,1.03, and 0.08, respectively, for the testing set, and 1, 1.04, and 0.11, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the LSSVM was accurate in predicting the pile bearing capacity. The results of comparison also showed that the proposed algorithm predicted the pile bearing capacity more accurately than the traditional methods including the GEP model.     

Prediction of engine performance and exhaust emissions with different proportions of ethanol–gasoline blends using artificial neural networks

The main purpose of this study is to experimentally investigate the use of ANNs (artificial neural networks) modelling to predict engine power, torque and exhaust emissions of a spark ignition engine which operates with gasoline and methanol blends. For the ANN modelling, the standard back-propagation algorithm was found to be the optimal choice for training the model. Afterwards, the performance of the ANN predictions was evaluated with the experimental results by comparing the predictions. Fuel type and engine speed have been used as the input layer, while engine torque, power, exhaust emissions, Tex and BSFC have also been used separately as the output layer. It was found that the ANN model is able to predict the engine performance, exhaust emissions, Tex and BSFC with a correlation coefficient of 0.9991887425, 0.9990868573, 0.9986749623, 0.9988624137, 0.9976761492, 0.9992943894 and 0.9978899033 for the Power, Torque, CO, CO₂, HC, Tex and BSFC for testing data, respectively.     

基于CPTu数据的黏性土不排水抗剪强度#br# 神经网络预测

利用人工神经网络模型,建立基于孔压静力触探（CPTu）现场测试数据的黏性土不排水抗剪强度的预测方法。为建立和验证人工神经网络模型,在3个场地开展CPTu和十字板剪切现场测试,共取得33个测孔的CPTu试验数据和相对应的不排水抗剪强度实测值。通过对比分析不同输入向量、不同网络隐层数、不同神经元数及不同改进算法对人工神经网络模型性能的影响,确定人工神经网络模型的具体形式。通过对训练组数据开展机器学习,所建立的人工神经网络模型能够有效地基于CPTu获得的端阻力和孔隙水压力现场测试数据对黏土不排水抗剪强度进行预测,预测结果与十字板剪切试验实测结果非常接近。与传统用于估算不排水强度的经验关系相比,采用人工神经网络模型预测结果与实测结果相关性显著提高、误差明显降低。     

基于灰色系统理论的桩基工程造价预测研究

对桩基工程进行造价分析,将灰色系统理论应用于柱基工程的造价预测中;结合某桩基工程造价实例,以历史资料为依据建立灰色预测模型,对人工及主要材料价格进行分析预测;并采用小误差概率、标准差比以及相对误差作为预测模型的精度检验指标,评价预测结果的可靠性;最后,将预测结果与实际结果进行对比得出了有益结论;本文思路与方法可为同类工...     

Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter

This study deals with predicting the performance characteristics of a reversibly used cooling tower (RUCT) under cross flow conditions for heat pump heating system in winter using artificial neural network (ANN) technique. For this aim, extensive field experimental work has been carried out in order to gather enough data for training and prediction. After back-propagation (BP) training combined with principal component analysis, the three-layer ANN model with a tangent sigmoid transfer function at hidden layer with 11 neurons and a linear transfer function at output layer was obtained. The predictions agreed well with the experimental values with a satisfactory correlation coefficient in the range of 0.9249-0.9988, the absolute fraction of variance in the range of 0.8753-0.9976, and the mean relative error in the range of 0.0008-0.54%, moreover, the root mean square error values for the ANN training and predictions were very low relative to the range of the experiments. The results reveal that ANN model can be used effectively for predicting the performance characteristics of RUCT under cross flow conditions, then providing the theoretical basis on the research of heat and mass transfer inside RUCT, which is important for design and running control of the RUCT system.     

Pipe pile setup: Database and prediction model using artificial neural network

Over the last few years, artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, the ability to accurately predict pile setup may lead to more economical pile design, resulting in a reduction in pile length, pile section, and size of driving equipment. In this paper, an ANN model was developed for predicting pipe pile setup using 104 data points, obtained from the published literature and the author's own files. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum ANN model.Finally, the paper compares the predictions obtained by the ANN with those given by a number of empirical formulas. It is demonstrated that the ANN model satisfactorily predicts the measured pipe pile setup and significantly outperforms the examined empirical formulas.     

Modeling oblique load carrying capacity of batter pile groups using neural network,random forest regression and M5 model tree

M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches were used to predict oblique load carrying capacity of batter pile groups using 247 laboratory experiments with smooth and rough pile groups. Pile length (L), angle of oblique load (α), sand density (ρ), number of batter piles (B), and number of vertical piles (V) as input and oblique load (Q) as output was used. Results suggest improved performance by RF regression for both pile groups. M5 model tree provides simple linear relation which can be used for the prediction of oblique load for field data also. Model developed using RF regression approach with smooth pile group data was found to be in good agreement for rough piles data. NN based approach was found performing equally well with both smooth and rough piles. Sensitivity analysis using all three modelling approaches suggest angle of oblique load (α) and number of batter pile (B) affect the oblique load capacity for both smooth and rough pile groups.     

Modeling corrosion currents of reinforced concrete using ANN

In this study, the mechanical properties of concretes are determined and the corrosion performances of steel that is embedded in concrete are analyzed by impressed voltage test. Different types of cements are used to prepare the concrete specimens with 0, 10, 20% fly ash. Corrosion currents of each specimen are measured and collected in five minute intervals using a data logger. The corrosion currents are modeled using feed forward artificial neural networks (ANNs). Measured results are then compared with the modeled ones in terms of root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient criterion. It is concluded that using composite cement or fly ash instead of cement, the durability of concrete against the effects of corrosion is improved considerably. It is also concluded that using ANNs, accurate modeling results for corrosion currents can be obtained.     

基于BP神经网络的堆肥物料抗剪强度预测模型

针对目前传统方式采集堆肥物料抗剪强度数据过程中环境恶劣、数据采集困难、试验误差大等问题,提出一种基于BP神经网络的抗剪强度预测模型。通过现场试验得到堆肥物料抗剪强度和堆体高度、温度、含水率、密度等参数共39组有效数据,以其中35组作为训练样本,其余4组用于评价模型的预测性能。结果表明,该模型预测值与实测值的平均误差为11. 35%,基于BP神经网络的抗剪强度预测模型具有较高的预测精度,为抗剪强度的预测提供了一种新方法。     

Artificial neural network based modelling of desiccant wheel

In this article, the concept of artificial neural network and goal oriented design have been used to propose a computer design tool that can help designers to evaluate performance of desiccant cooling system and behaviour of the desiccant wheel. Based on the experimental observations on desiccant wheel, a neural network model has been developed using a neural network toolbox of MATLAB^® with feed forward back propagation method. The model has been validated against experimental data sets. A number of training algorithms with feed forward back propagation method have been used for the modelling of desiccant wheel to identify a training algorithm with least mean square error (MSE). The performance of all training algorithms has been analyzed and training algorithm trainlm (Levenberg-Marquardt back propagation) is found most suitable for the prediction of outputs which have least mean square error of 0.064462 and 0.007575 for specific humidity and temperatures respectively. The proposed model can predict the specific humidity and temperature at the outlet of desiccant wheel within the range of experimental values.     

Whole model empirical validation on a full-scale building

This paper describes an empirical validation study undertaken on two identical full-size buildings within the scope of the IEA ECB Annex 58 project. Details of the experimental configuration and monitoring are included, together with results from measurements and from predictions made by 21 modelling teams using commercial and research simulation programmes. The two-month, side-by-side experiment was undertaken on buildings with high levels of thermal mass and in a period with high solar gains. The detailed specification and associated measurement data provide a useful empirical validation dataset for programme testing. Results from the modelling demonstrate good agreement between measured data and predictions for a number of programmes, in both absolute predictions of temperatures and heat inputs as well as dynamic response. On the other hand, a significant number of user input errors resulted in poor agreement for other programmes, especially in the blind validation phase of the modelling methodology.     

大直径钢管桩竖向承载力计算方法研究

随着海上油气资源的深度开发与海上风电工程的投资建设,大直径钢管桩基础以其结构简单、安装便利、承载性能优越的独特优势被广泛使用。随着钢管桩直径的增加,其承载模式有别于传统的小直径桩。分析了太沙基、梅耶霍夫、别列柴策夫深基础承载力理论公式与建筑桩基技术规范、港口工程桩基规范、API、DNV规范以及考虑土拱效应的计算方法之间的差异性,并应用上述方法计算了实际工程中大直径钢管桩的竖向承载力,将计算结果与动测结果进行对比。结果表明:理论算法中太沙基法与梅耶霍夫法计算值较大,别列柴策夫公式和考虑土拱效应计算法相对准确;建筑桩基技术规范、港口工程桩基规范相对于API、DNV规范更加保守。     

诠释基桩承载力之波动方程分析应用

基桩承载力之试验方法包括常见之静载重试验和近期发展出的高应变动力试桩。其中静载重试验所耗费人力及物力颇高,施做过程亦具危险性,经常无法得到完整的试桩曲线,造成诠释之不便与困扰。鉴于此,本研究采波动方程分析仿真桩载重试验,配合现地土壤之标准贯入值,藉迭代方式修正材料性质,以使分析结果合乎试验资料,并建立外插程序方便各式诠释法使用,其结果经验证符合实际状况。     

Artificial neural networks to predict daylight illuminance in office buildings

A prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance.     

带有偏差单元的IRN模型在深层搅拌桩承载力计算中的应用

对影响深层搅拌桩复合地基承载力的因素进行了分析 ,并对现行的设计方法存在的问题进行探讨 ,提出利用人工神经网络带有偏差单元的IRN(InternallyRecurrentNet)模型对复合地基承载力进行计算的新思路。通过实例验证 ,该模型可达到较好的效果 ,为今后深层搅拌桩承载力设计计算提供了可借鉴的方法。     

基于 SVR 的工程建设项目快速投资估算方法研究

在建设项目前期,如何快速而准确地估算工程项目的造价,对项目的投资决策具有很大的意义。针对传统造价估算 方法的不足之处,采用 SPSS 统计分析软件进行工程造价指标的相关性分析及指标体系选取,将之作为输入变量,使用真实 案例训练集样本训练 SVR 模型并进行仿真模拟预测。为了验证提出的 SVR 模型的有效性,引入 BP 人工神经网络来进行预 测结果的对比验证。结果表明,SVR 模型得到的预测值平均绝对百分比误差约为 5%,拟合优度 R2高达 0.97,远小于 BPNN 模型的预测误差 14%,即提出的 SVR 估算模型要比 BP 人工神经网络预测模型具有更良好的泛化能力,预测精度更高,因 此其在工程项目前期投资估算实践中具有一定的现实意义。     

Slepian models for waves and wave-structure interaction

A regressive form of Slepian modelling was used to develop predictive models for the key variables associated with three quite different experimental data sets. The first data set provided a time series record of the surface elevation for a moderate random seaway. The second data set provided measurements of the wave elevation at the front of a finite draft deep water platform. This data set was significantly more nonlinear since the local wave field was amplified by the presence of the platform. The final data set dealt with the rate of wave run-up and this involved the derivative of the second data set. The results consistently illustrated the need to have an adequate number of events to use as the basis for the regression model. The study presents guidelines for selecting the initial crossing level which is crucial to the Slepian model development. Once the regression model has been established the model allows one to make predictions for other values of level crossing. It was found that the accuracy of those predictions depends on the accuracy of the initial regression process and that reasonable estimates can be obtained.     

Developing cation exchange capacity and soil index properties relationships using a neuro-fuzzy approach

Artificial intelligence methods are employed to predict cation exchange capacity (CEC) from five different soil index properties, namely specific surface area (SSA), liquid limit, plasticity index, activity (ACT), and clay fraction (CF). Artificial neural networks (ANNs) analyses were first employed to determine the most related index parameters with cation exchange capacity. For this purpose, 40 datasets were employed to train the network and 10 datasets were used to test it. The ANN analyses were conducted with 15 different input vector combinations using same datasets. As a result of this investigation, the ANN analyses revealed that SSA and ACT are the most effective parameters on the CEC. Next, based upon these most effective input parameters, the fuzzy logic (FL) model was developed for the CEC. In the developed FL model, triangular membership functions were employed for both the input (SSA and ACT) variables and the output variable (CEC). A total of nine Mamdani fuzzy rules were deduced from the datasets, used for the training of the ANN model. Minimization (min) inferencing, maximum (max) composition, and centroid defuzzification methods are employed for the constructed FL model. The developed FL model was then tested against the remaining datasets, which were also used for testing the ANN model. The prediction results are satisfactory with a determination coefficient, R ² = 0.94 and mean absolute error, (MAE) = 7.1.     

Predicting a water infrastructure leakage index via machine learning

In this study, the infrastructure leakage index (ILI) indicator that is preferred frequently by the water utilities with sufficient data to determine the performances of water distribution systems is modeled for the first time through the three different methodologies using different input data. In addition to the variables in the literature used for the classical ILI calculations, the age parameter is also included in the models. In the first step, the ILI values have been estimated via multiple linear regression (MLR) using water supply quantity, water accrual quantity, network length, service connection length, number of service connections, and pressure variables. Secondly, the Artificial Neural Network (ANN) approach has been applied with raw data to improve the ILI prediction performance. Finally, the data set has been standardized with the Z-Score method for increasing the learning power of the ANN models, and then the ANN predictions have been made by converting the data through the principal component analysis (PCA) method to minimize complexity by reducing the data set size. The model predictions have been evaluated via mean square error, G-value, mean absolute error, mean bias error, and adjusted-R² model performance scale. When the model outputs obtained at the end of the study are evaluated together with the classical ILI calculations, it is seen that the successful ILI predictions with three and four variables, including the age parameter, rather than six variables, have been made through the PC-ANN method. Water utilities with insufficient physical and operational data for ILI indicator calculation can make network performance evaluations by predicting the ILI through the models suggested in this study with high accuracy in a reliable way.     

基于小波概率神经网络的单桩竖向承载力预测模型及应用研究

分析小波概率神经网络(WPNN)与数据融合技术在预测单桩竖向承载力中的应用原理,建立基于小波概率神经网络和数据融合技术的预测模型。根据长期的工程实测资料,利用高层建筑物静载试验数据对模型进行检验,并选取典型的样本进行预测值的误差分析。结果表明,预测的结果与静载试验数据吻合较好,从而证实了WPNN预测方法具有较好的可靠性和工程应用价值。