A support vector machine model for contractor prequalification

In complex and high value projects, prequalification is crucial for both contractors and clients, as it targets towards best value delivery through qualification safeguards and streamlined competition among potential candidates. Due the complex nature of the procurement problems such as prequalification exercises, the robust models are rarely attempted. The research reported in this paper presents an overview of potential suitability of Support Vector Machine (SVM) method for contractor/consultant prequalification transactions in the construction project procurements. Furthermore, the performance of SVM is compared with specific artificial neural network outcomes. The results obtained from practical datasets indicate encouraging potentials for SVM applications in the procurement problems such as prequalification and contractor selection. Hence, a SVM-based decision support framework is proposed.     

基于聚类分析和支持向量机的滑坡易发性评价

在将支持向量机(support vector machine,SVM)等机器学习模型用于区域滑坡易发性评价时,大都随机或主观地选取非滑坡栅格单元,不能保证所选的非滑坡栅格单元是真正的"非滑坡"。为解决此问题,提出基于聚类分析和SVM的滑坡易发性评价模型。该模型首先用自组织映射(self-organizing mapping,SOM)神经网络对滑坡易发性进行聚类分析;然后从极低易发区中选择非滑坡栅格单元,确保所选非滑坡栅格单元是高概率的"非滑坡";最后采用SVM模型基于已知滑坡、所选非滑坡和环境因子对滑坡易发性进行评价。将提出的SOM-SVM模型用于三峡库区万州区滑坡易发性评价,并将得到的易发性结果与随机选取非滑坡的单独SVM模型结果做对比。结果显示SOM-SVM模型具有比单独SVM模型更高的成功率和预测率,表明SOM神经网络能更准确地选取非滑坡栅格单元。     

Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine

This paper investigates structural reliability analysis with both random and interval variables, which is defined as a three‐classification problem and handled by support vector machine (SVM). First, it is determined that projection outlines on the limit‐state surface are crucial for describing separating hyperplanes of the three‐classification problem. Compared with the whole limit‐state surface, the region of projection outlines are much smaller. It will be beneficial to reduce the number of update points and the computational cost if SVM update concentrates on refining the approximate projection outlines. An adaptive local approximation method is developed to realize that the initial built SVM model is sequentially updated by adding new training samples located around the projection outlines. Using this method, the separating hyperplanes can be accurately and efficiently approximated by SVM. Finally, a new method is proposed to evaluate the failure probability interval based on Monte Carlo simulation and the refined SVM.     

支持向量机在隧洞超挖预测中的应用

介绍了支持向量机算法及围岩破坏模式识别的支持向量机算法,利用支持向量法分类算法对隧道围岩超挖块体的大小进行了分类,并建立了预测模型,计算结果表明用支持向量机能较好地预测超挖块体的大小。     

A morphological approach to pipe image interpretation based on segmentation by support vector machine

This paper presents a new approach to automatic pipe inspection using pixel-based segmentation of colour images by support vector machine (SVM) coupled with morphological analysis of the principal connected component of the segmented image. The pixel-based segmentation method has been tested using RGB, HSB, Gabor and local window feature sets and is seen to work best with the HSB feature set. The morphological analysis allows the principal connected component of the segmented image to be decomposed into the pipe flow line region, the pipe joints and adjoining defects. Generalisations of the morphological operations of erosion and dilation are defined and some simple properties of them are derived. A fuzzy approach to pipe connection detection is also described.     

Reliability analysis of tunnel using least square support vector machine

In the reliability analysis of tunnels, the limited state function is implicit and nonlinear, and is difficult to apply based on the traditional reliability method, especially for large-scale projects. Least squares support vector machines (LS-SVM) are capable of approximating the limited state function without the need for additional assumptions regarding the function form, in comparison to traditional polynomial response surfaces. In the present work, the LS-SVM method was adapted to obtain the limited state function. An LS-SVM-based response surface method (RSM), combined with the first-order reliability method (FORM), is proposed for use in tunnel reliability analysis and implementation of the method is described. The reliability index obtained from the proposed method applied to particular tunnel configurations under different conditions shows excellent agreement with Low and Tang’s (2007) method and traditional RSM results, and indicates that the LS-SVM-based RSM is an efficient and effective approach for reliability analysis in tunnel engineering.     

基于超球支持向量机的水环境质量评价

将超球向量机方法引用到水环境质量评价中,并应用该方法与标准指数法分别对细河水体进行了评价。结果表明,超球向量机评价方法对监测值处于分类重叠区域的水质分类要优于标准指数法,评价结果更客观,具有较好的泛化能力,说明超球向量机法可以用于水环境质量评价,具有很好的研究前景。     

边坡可靠性评价的向量投影响应面研究及应用

岩土体的力学参数存在一定的随机性,当进行边坡稳定可靠性评价时,当功能函数不能用显式表达时,响应面法成为计算可靠度指标的重要方法。将向量投影响应面法与有限元强度折减法相结合,研究隐式功能函数边坡工程稳定可靠度计算方法。首先利用强度折减法结果拟合稳定系数计算方程从而代替隐式方程,并建立极限状态方程,然后进行向量投影以确定展开点和抽样点,进行近似方程的拟合、验算点和可靠度指标的计算,直到计算达到收敛。通过与不同计算方法进行对比,表明区别于通常以插值点为中心展开生成样本点的向量投影取样法的准确性和合理性,该方法可使可靠性分析次数显著减少,改善了对非线性程度较高的极限功能函数求解可靠指标的收敛性。同时,将该方法应用于杭兰高速公路大水田边坡的稳定可靠性分析当中。     

基于机器语言的建筑混凝土设计

建筑混凝土的强度受多种因素的影响,其强度的预测是一个多指标综合复杂问题。基于机器算法支持向量机建立了建筑混凝土的强度设计与预测的支持向量机模型,其中模型参数通过粒子群算法进行选择和优化。将建立的模型计算结果与实测混凝土28 d抗压强度进行比较,讨论了各因素与强度值之间的关系。研究表明:预测结果与实测结果一致,可见该模型可以很好的为混凝土设计提供依据。     

Project success prediction using an evolutionary support vector machine inference model

Accomplishing construction projects successfully requires continuous monitoring and control by construction managers of factors critical to project success. This research proposed using an Evolutionary Support Vector Machine Inference Model (ESIM) to predict project success dynamically. ESIM is a hybrid that integrates a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). SVM is concerned primarily with learning and curve fitting, while fmGA deals primarily with optimization. Furthermore, the model integrates the process of Continuous Assessment of Project Performance (CAPP) to select factors that influence project success. Training and test patterns were collected from a CAPP database of 46 construction projects. These projects represent real data collected by Russell from 16 company members of the Construction Industry Institute (CII). Results show that ESIM is able to predict project success at a significant level of accuracy.     

Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme

For ground-level ozone (O(3)) prediction, a predictive model, with reliable performance not only on non-polluted days but, more importantly, on polluted days, is favored by public authorities to issue alerts, so that concerned citizens and industrial organizations could take precautions to avoid exposure and reduce harmful emissions. However, the class imbalance problem, i.e., in some collected field data, number of O(3) polluted days are much smaller than that of non-polluted days, will deteriorate the model performance on minority class-O(3) polluted days. Despite support vector machine (SVM) obtaining promising results in air quality prediction, in this study, a cost-sensitive classification scheme is proposed for the standard support vector classification model (S-SVC) in order to investigate whether the class imbalance plagues S-SVC. The S-SVC with such scheme is named as CS-SVC. Experiments on imbalanced data sets collected from two air quality monitoring sites in Hong Kong show that 1) S-SVC is still sensitive to class imbalance problem; 2) compared with S-SVC, CS-SVC effectively avoids class imbalance problem with lower percentage of false negative on O(3) polluted days but with higher percentage of false positive on non-polluted days; 3) compared with both S-SVC and CS-SVC, support vector regression model (SVR), after converting its output to binary one, only has similar performance with S-SVC, which indicates class imbalance problem also impairs the regressor model. From point of protecting public health, CS-SVC, which less likely misses to forecast O(3) polluted days, is recommended here.     

Evolutionary support vector machine inference system for construction management

Problems in construction management are complex, full of uncertainty, and vary based on site environment. Two tools, the fast messy genetic algorithms (fmGA) and support vector machine (SVM), have been successfully applied to solve various problems in construction management. Considering the characteristics and merits of each, this paper combines the two to propose an Evolutionary Support Vector Machine Inference Model (ESIM). In the ESIM, the SVM is primarily employed to address learning and curve fitting, while fmGA addresses optimization. This model was developed to achieve the fittest C and γ parameters with minimal prediction error. This research further integrates the developed ESIM with an object-oriented (OO) computer technique to create an Evolutionary Support Vector Machine Inference System (ESIS). Simulations conducted to demonstrate the robustness of the model in application indicate that ESIS may be used as a multifarious intelligent decision support system in decision-making to help solve a wide range of construction management problems.     

建筑分包队伍选择的支持向量机方法

建筑施工质量受施工队伍素质的影响很大,由于存在着信息不对称,目前许多建筑企业对施工队伍的选择往往缺乏科学的依据和方法,支持向量机的方法具有小样本量,模型构造有坚持的理论依据等特点,应用这种方法构造了一个建筑分包队伍选择模型,提高了建筑分包队伍选择的可靠性。     

Forecasting enterprise resource planning software effort using evolutionary support vector machine inference model

Despite significant advances in procedures that facilitate project management, the continued reliance of software managers on guesswork and subjective judgment causes frequent project time overruns. This study uses an Evolutionary Support Vector Machine Inference Model (ESIM) for efficiently and accurately estimating the person-hour of ERP system development projects. The proposed ESIM is a hybrid intelligence model integrating a support vector machine (SVM) with a fast messy genetic algorithm (fmGA). The SVM mainly provides learning and curve fitting while the fmGA minimizes errors. The analytical results in this study confirm that, compared to artificial neural networks and SVM, the proposed ESIM provides preliminary prediction at early phase of ERP software development effort for the manufacturing firms with superior accuracy, shorter training time and less overfitting. Future research can develop user-friendly expert systems with window or browser interfaces that can be used by planning personnel to flexibly input related variables and to estimate development effort and corresponding project time/cost.     

Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine

The phenomenon of soil liquefaction is one of the most complex and interesting fields in geotechnical earthquakes that has drawn the attention of many researchers in recent years. The present study used hybrid particle swarm optimization and genetic algorithms with a fuzzy support vector machine (FSVM) as the classifier for the soil liquefaction prediction problem. Fuzzy logic is used to decrease the outlier sensitivity of the system by inferring the importance of each sample in the training phase to increase the ability of the classifier’s generalization. Using the appropriate combination of optimization algorithms, we can find the best parameters for the classifier during the training phase without the need for trial and error by the user due to the high accuracy of the classifier. The proposed approach was tested on 109 CPT-based field data from five major earthquakes between 1964 and 1983 recorded in Japan, China, the USA and Romania. Good results have been demonstrated for the proposed algorithm. Classification accuracy is 100% for the combination of the optimization algorithms with the FSVM classifier. The results show that the best kernel used in the training of the FSVM classifier is a radial basis function (RBF). According to the experimental results, the proposed algorithm can improve classification accuracy and that it is a feasible method for predicting soil liquefaction using the optimal parameters of the classifier with no user interface.

     

An enforced support vector machine model for construction contractor default prediction

The financial health of construction contractors is critical in successfully completing a project, and thus default prediction is highly concerned by owners and other stakeholders. In other industries many previous studies employ support vector machine (SVM) or other Artificial Neural Networks (ANN) methods for corporate default prediction using the sample-matching method, which produces sample selection biases. In order to avoid the sample selection biases, this paper used all available firm-years samples during the sample period. Yet this brings a new challenge: the number of non-defaulted samples greatly exceeds the defaulted samples, which is referred to as between-class imbalance. Although the SVM algorithm is a powerful learning process, it cannot always be applied to data with extreme distribution characteristics. This paper proposes an enforced support vector machine-based model (ESVM model) for the default prediction in the construction industry, using all available firm-years data in our sample period to solve the between-class imbalance. The traditional logistic regression model is provided as a benchmark to evaluate the forecasting ability of the ESVM model. All financial variables related to the prediction of contractor default risk as well as 7 variables selected by the Multivariate Discriminant Analysis (MDA) stepwise method are put in the models for comparison. The empirical results of this paper show that the ESVM model always outperforms the logistic regression model, and is more convenient to use because it is relatively independent of the selection of variables. Thus, we recommend the proposed ESVM model as an alternative to the traditionally used logistic model.     

A least-squares support vector machine approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing systems

Precise estimation of temperature variations throughout gas production systems can enhance designing the production amenities. Routine methods for determining the temperature profiles in gas production systems are based on the gas composition and flash calculations. However, if the gas compositions are not available, the gas production system can be modelled by employing a black-oil approach, which is also a method for calculating the oil/gas resources and for modelling the gas reservoir operation. Accordingly, for black-oil models and when the natural gas compositions are not accessible, applying robust predictive tools in this research is of high interest in natural production systems. The current study places emphasis on applying the predictive model based on the least- squares support vector machine (LSSVM) to estimate precisely the proper temperature drop associated with a given pressure drop throughout the natural gas production systems based on the black-oil approach to acquire an accurate result for the temperature drop of natural gas streams. Genetic algorithm was used to optimise hyper-parameters (γ and σ²) which are embedded in the LSSVM model. Using this method is simple and it accurately determines the temperature drop through the natural gas stream with minimum uncertainty.     

Prediction performance of natural gas dehydration units for water removal efficiency using a least-square support vector machine

Natural gas dehydration unit is employed to eliminate water from natural gas liquids and natural gas, and it is needed to avoid condensation of free water and creation of hydrates in transportation and processing facilities, prevent corrosion, and meet a water content condition. In this paper, a least-square support vector machine (LSSVM) coupled with genetic algorithm (GA) was employed to estimate the water dew point of a natural gas stream in equilibrium with a triethylene glycol (TEG) solution at different TEG concentrations and temperatures. Results showed that GA–LSSVM accomplishes more reliable outputs compared with real recorded data in terms of statistical criteria.