基于案例模糊推理估算施工成本系统设计研究

在基于案例模糊推理的基础上,结合施工成本估算的特点,提出了建立在案例模糊推理上的施工成本快速估算系统设计思路,并给出了估算的系统框架和流程图,为以后的估算奠定了科学的基础。     

A case-based reasoning cost estimating model using experience by analytic hierarchy process

A case-based reasoning model is proposed, where experience is included in all processes of construction cost estimating by the analytic hierarchy process. The model overcomes the difficulty of measuring experience for determining the weights of attributes. The accuracy of three different models was compared. The model using the analytic hierarchy process was more accurate, reliable, and explanatory than the other models, and closer to the original aim of the case-based reasoning model, for solving new problems using experience from previous cases.     

基于遗传神经网络的公路工程造价估算分析

从影响公路造价的影响因素中提取特征因子为参数,用历史数据为依据,用遗传神经网络建立了公路造价快速估测模型,最后用实例验证了遗传神经网络模型在公路工程造价估测中的优良效果。     

Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models

It is commonly perceived that how well the planning is performed during the early stage will have significant impact on final project outcome. This paper outlines the development of artificial neural networks ensemble and support vector machines classification models to predict project cost and schedule success, using status of early planning as the model inputs. Through industry survey, early planning and project performance information from a total of 92 building projects is collected. The results show that early planning status can be effectively used to predict project success and the proposed artificial intelligence models produce satisfactory prediction results.     

基于范例推理的隧道渗漏初步评价模型

针对隧道工程渗漏的成因,综合考虑围岩特性、地下地表水作用、施工方法和环境条件等因素,确定了基于多因素的隧道渗漏评价指标体系,在此基础上,建立了隧道工程渗漏范例库,研究并构建了基于范例推理的隧道渗漏评价模型,最后利用构建的模型和范例库,分析评价了某隧道工程的渗漏状况,验证了该评价模型的有效性。     

基于回归分析方法对于工程造价运动因素影响的分析

从经济角度看,建筑市场的竞争主要是工程造价的竞争,谁在工程建设中既能确保工期、质量,又能注重成本管理,控制和把握合理的造价,以最小的成本换取最大的效益,谁就能在竞争中取得主动,走向成功。本文分别从定性、定量两个角度去剖析我国建筑业的发展现状,运用spss软件研究工程造价运动中对于造价影响比重较大的因素,运用Kouskoulas回归估算模型建模,模拟函数,以研究出因素的比重关系。从而推断出工程造价过程中影响造价的因素之间的相关性和关联性。     

Predicting final cost for competitively bid construction projects using regression models

This paper studies the relationship between the low bid and completed cost for competitively bid highway projects. Data are analyzed from several agencies managing highway and dredging projects. A natural log transformation of the low bid and final cost was found to produce regression models for each agency that had high R and R² values. These models can predict completed project cost using only the project low bid as input. A separate regression model was determined for each agency. Based on the form of the calculated models it appears that highway agencies construct projects where the final cost increases as an increasing percentage of the low bid price as the project magnitude increases. The dredging projects appear to follow different mechanisms of cost increase than the highway projects and were not predicted as well by the regression models. The regression models can also be used for budgeting purposes by submitting the sum of the low bids for a group of projects as input. The regression models output a prediction of the cost of the group of projects that was found to be highly accurate.     

The attribute impact concept: Applications in case-based reasoning and parametric cost estimation

The success of every construction project depends on the satisfactory achievement of a client's needs relating to cost, duration, and quality. Among them, costs must be managed with special awareness. In an effort to improve the estimate accuracy of cost during the initial stages of a building project, this research introduces the concept of ‘attribute impact’ (AI), which can measure the weights of attributes quantitatively and prioritize them. This study will also explain AI development, which adopts the impulse–momentum theorem of physics. For a case study, the project analyzes 163 public apartment buildings from 15 housing complex projects in Korea. To examine the validity of the proposed AI, the case study carries out two types of validation in terms of estimate accuracy using the parametric method and the case-based reasoning (CBR) applicability test. The validation results support the acceptable use of the suggested AI in measuring the weights of attributes and its estimate accuracy when combined with parametric or CBR estimation.     

Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens

A comparison study has been performed with neural networks (NNs) and multiple linear regression models to forecast the next day's maximum hourly ozone concentration in the Athens basin at four representative monitoring stations that show very different behavior. All models use 11 predictors (eight meteorological and three persistence variables) and are developed and validated between April and October from 1992 to 1999. Performance results based on a wide set of forecast quality measures indicate that the NNs provide better estimates of ozone concentrations at the monitoring sites, whilst the more often used linear models are less efficient at accurately forecasting high ozone concentrations. The violation of the European information threshold of 180 microg/m(3) is successfully predicted by the NN in 72% of the cases on average. Results at all stations are consistent with similar ozone forecast studies using NNs in other European cities.     

基于神经网络理论的机场沥青道面状况评定

为了简便、有效地进行机场沥青道面状况的评定,应用神经网络理论建立了道面平整度、摩擦系数与道面状况指数PCI的网络模型。通过对国内12个机场道面平整度、摩擦系数的实测和道面损坏状态的调查,所得数据作为样本对网络进行训练。结果表明,当目标误差为1×10-4时,计算值与实测值间的相对误差均小于1.8%。     

Evaluating landslide susceptibility based on cluster analysis,probabilistic methods,and artificial neural networks

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting.

     

Modeling the slake durability index using regression analysis, artificial neural networks and adaptive neuro-fuzzy methods

Clay bearing, weathered and other weak rocks cause major problems in engineering practice due to their interactions with water. The slake durability index (I _d2) is an important tool used to assess the resistance of these rocks to erosion and degradation, but sample preparation for this test is tedious. The paper reports an attempt to define I _d2 through statistical models using other parameters that are simpler to obtain. The main objective of this study was to define the best empirical relationship between the I _d2 and the point load strength index (I _s(50)), dry unit weight (γ _d) and fractal dimension (D) parameters of eight rock types by applying general multiple linear regression (GLM), artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The models obtained were evaluated using the R ², MSE, MARE and d parameters. The results indicate that the relationships between I _d2 and γ _d, I _s(50) and D were best obtained using ANN, followed by GLM and ANFIS. It is concluded that ANN modelling is a fast and practical method of establishing I _d2.     

基于建筑模型的施工项目成本分析方法初探

分析了基于建筑模型的施工成本分析方法达到的效果及其优势,介绍了基于模型的施工项目成本分析方法操作流程,并对该成本分析方法实施的技术支撑进行了归纳总结,以合理控制施工成本。     

采用性别相关的深度神经网络及非负矩阵分解模型用于单通道语音增强

为了从带噪信号中得到纯净的语音信号,提出了一种采用性别相关模型的单通道语音增强算法。具体而言,在训练阶段,分别训练了与性别相关的深度神经网络-非负矩阵分解模型用于估计非负矩阵分解中的权重参数;在测试阶段,提出了一种基于非负矩阵分解和组稀疏惩罚的算法用于判断测试语音中说话人的性别信息,然后再采用对应的模型估计权重,并结合已训练好的字典进行语音增强。实验结果表明所提算法在噪声抑制量及语音质量上,均优于一些基于非负矩阵分解的算法和基于深度神经网络的算法。     

中英两国建筑安全管理模式的比较研究

为健全建筑安全管理体制,提高我国建筑安全管理水平,采用比较法对中、英两国建筑安全的相关法律法规、安全管理体系、安全文化建设、安全投入与培训制度、安全管理理念等方面进行深入分析。指出当前国内建筑安全管理方面存在的主要不足,具体涉及：缺乏完整的建筑安全管理法律法规,缺乏清晰明了的监管机构组织,缺乏必要的安全培训与教育,缺乏对新型建筑理念的敏锐洞察力等。最后借鉴英国建筑安全管理模式和经验,提出相应的改进措施改进和完善建筑业安全管理模式,为我国今后建筑业的发展指明了方向。     

节能建筑与传统建筑造价测算分析方法的比较

通过对多个典型工程在采用和不采用节能技术或产品的工程造价进行了测算、对比和分析,得出了节能建筑既节能又节钱的结论,指出节能建筑还大大降低了房屋使用的费用,以消除人们对推行节能建筑的种种忧虑。     

Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

Underground mining becomes more efficient due to the technological advancements of drilling and blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA and NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters, respectively. The performance of LMRA, NMRA, and optimized ANN models was evaluated by comparing coefficient correlations (R²) and their values are 0.694, 0.704 and 0.945, respectively, which means that the relatively high level of accuracy of the optimized ANN in comparison with LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements.     

基于Hopfield神经网络的结构优化分析

探讨了Hopfield网络在土木工程优化中应用的途径，并简要介绍了与Hopfield网络密切相关的模拟退火技术，介绍了用于工程结构优化的连续型Hopfield网络的能量函数，该能量函数能够准确地描述结构优化问题。同时，探讨了Hopfield网络用于工程结构优化的机理。然后，研究了基于CHNN的工程结构优化在Matlab上的实践，探讨了网络参数的选取。最后，通过算例表明方法的有效性。     

Benefit of splines and neural networks in simulation based structural reliability analysis

Simulation based algorithms are often used to calculate an accurate value for the system reliability of complex systems. These concepts are very appealing because of their inherent simplicity. This is even more emphasized when used in combination with an implicit limit state function, for which the outcome is calculated by means of a finite element analysis. One of the major disadvantages however is the large number of simulations required to obtain an accurate estimate of the failure probability. This might result in an unrealistic processing time, making the method unusable for practical purposes. To meet this disadvantage, reliability analysis based on simulation methods in combination with an adaptive low order polynomial response surface are often used. The applicability has been demonstrated extensively. Ideally, no functional form is preset. The objective of this paper is to further increase the efficiency of simulation based reliability algorithms. Therefore the low order polynomial response surfaces are extended using neural networks and splines. The reliability framework is presented, compared with traditional response surface methods and commented extensively. The overall behaviour of the technique is addressed referring to several benchmark examples.