基于形貌特征的CO2腐蚀速率预测方法

针对CO2腐蚀过程复杂，难以利用实测数据有效预测腐蚀速率问题，文中以腐蚀形貌图像为对象，利用支持向量机（SVM）构建预测模型，实现对CO2腐蚀速率的预测。对N80钢的CO2腐蚀图像进行灰度处理、灰度增强及二值化处理，提取蚀孔数目和孔蚀面积。经计算获得孔蚀密度及孔蚀率，结合工作温度及CO2分压作为腐蚀速率预测的四维特征向量。以SVM构建预测模型，经测试，可准确预测CO2腐蚀速率，并与神经网络预测结果进行对比，验证了该方法的优越性。     

Using survival models to estimate bus travel times and associated uncertainties

Transit agencies often provide travelers with point estimates of bus travel times to downstream stops to improve the perceived reliability of bus transit systems. Prediction models that can estimate both point estimates and the level of uncertainty associated with these estimates (e.g., travel time variance) might help to further improve reliability by tempering user expectations. In this paper, accelerated failure time survival models are proposed to provide such simultaneous predictions. Data from a headway-based bus route serving the Pennsylvania State University-University Park campus were used to estimate bus travel times using the proposed survival model and traditional linear regression frameworks for comparison. Overall, the accuracy of point estimates from the two approaches, measured using the root-mean-squared errors (RMSEs) and mean absolute errors (MAEs), was similar. This suggests that both methods predict travel times equally well. However, the survival models were found to more accurately describe the uncertainty associated with the predictions. Furthermore, survival model estimates were found to have smaller uncertainties on average, especially when predicted travel times were small. Tests for transferability over time suggested that the models did not over-fit the dataset and validated the predictive ability of models established with historical data. Overall, the survival model approach appears to be a promising method to predict both expected bus travel times and the uncertainty associated with these travel times.     

Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system

Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level.     

北京地铁隧道地表横向沉降槽参数分析

为解决北京地铁隧道施工不同影响区划分和影响范围确定的不准确问题,对北京地区13 条地铁线路、903 份隧道工程的地表横向沉降槽资料进行分析,根据施工方法和地层条件的不同,分别对盾构法和矿山法施工隧道在黏性土地层、砂卵石地层等区域的沉降槽Peck 公式拟合参数进行统计分析,得出地层损失率和宽度参数的分布形态、相关统计值以及与隧道相对埋深的相关性。研究结果表明: 1)地层损失率和宽度参数的数理统计结果可以很好地指导北京及类似地层条件的城市地铁隧道工程影响区划分和影响范围的确定; 2)施工方法和地层条件是影响地铁隧道周围地层变形的重要因素,工程地表变形控制应注重相关研究; 3)建议各地深入开展地铁隧道沉降槽的拟合分析研究,为隧道工程影响区划分和影响范围确定提供科学依据。     

基于混沌递进预测模型与趋势检验的深基坑变形规律研究

为提高基坑变形预测精度及稳定性,首先,利用遗传算法优化BP神经网络的结构参数,再将参数优化后的BP神经网络与灰色模型结合,构建出GA-BP神经网络模型,并利用该模型实现基坑变形序列的初步预测; 其次,基于残差序列的混沌特性,再利用混沌理论进行残差优化,进一步构建考虑混沌特性优化的GA-BP神经网络模型; 最后,将SR检验引入到基坑变形趋势判断中,以检验预测结果的准确性。实例检验表明： 通过遗传算法及混沌理论的递进优化,能逐步提高预测精度,验证文章预测模型的有效性,且预测结果与SR检验结果的一致性较好,说明该预测模型的可信度高。     

大瑞铁路高黎贡山隧道原岩温度预测

随着深埋长隧道需求数量的增加,高地温热害问题越来越突出,严重影响到施工人员的安全及工作效率,需要根据地温的分布特征来对隧道高温地段采取必要的降温措施。结合大瑞铁路高黎贡山隧道的地质、地温实测资料及通过“谷地地理信息系统”获取的地形数据,采用基于地质演化历史计算原岩温度的数学模型,编制相应的计算程序对高黎贡山大瑞铁路隧道进行原岩温度预测,并对隧址区的温度场沿洞线方向进行简单划分,为隧道采用有效的降温措施提供必要的地温场数据。通过计算分析可以得出： 1）沿隧道向瑞丽方向,隧道原岩温度有先增大后减小的趋势,最高点温度为31.73 ℃; 2）隧道开始大约2.5 km以内及18 km至隧道终点段,隧道岩温在28 ℃以下; 3）2.5~18 km段隧道岩温为28~31.73 ℃,其中高于28 ℃区域需要采取实时温度监控措施。     

引汉济渭秦岭隧洞高磨蚀性硬岩TBM滚刀磨损试验研究

引汉济渭工程秦岭输水隧洞TBM掘进施工在试掘进段因遭遇高磨蚀性硬岩地层,滚刀发生严重磨损,致使施工进度严重滞后。为降低刀具磨损,节约施工成本,加快施工进度,必须对刀具磨损进行准确预测。针对高磨蚀性地层,开展滚刀磨损预测研究; 通过课题组前期研究结果,建立滚刀磨损理论预测模型; 通过室内实验,建立滚刀磨损实验预测模型; 通过现场试验,对室内实验模型及课题组前期研究建立的理论预测模型进行验证。结果表明,实验预测结果具有较高的准确性,相对误差在10%以内。     

Rollover risk prediction of heavy vehicles by reliability index and empirical modelling

This paper focuses on a combination of a reliability-based approach and an empirical modelling approach for rollover risk assessment of heavy vehicles. A reliability-based warning system is developed to alert the driver to a potential rollover before entering into a bend. The idea behind the proposed methodology is to estimate the rollover risk by the probability that the vehicle load transfer ratio (LTR) exceeds a critical threshold. Accordingly, a so-called reliability index may be used as a measure to assess the vehicle safe functioning. In the reliability method, computing the maximum of LTR requires to predict the vehicle dynamics over the bend which can be in some cases an intractable problem or time-consuming. With the aim of improving the reliability computation time, an empirical model is developed to substitute the vehicle dynamics and rollover models. This is done by using the SVM (Support Vector Machines) algorithm. The preliminary obtained results demonstrate the effectiveness of the proposed approach.     

基于ARMA的桥梁监测信息预测技术研究

为解决桥梁结构健康监测中长期累积的海量监测数据处理问题,采用数据挖掘中的时间序列分析方法,利用自回归移动平均(ARMA)技术对桥梁历史静态监测量进行分析,直接得出关于其过去行为的有关结论,进而推断其未来发展趋势。与基于因果关系的结构式模型预测法不同,ARMA无需明确模型结构或边界条件,而是直接从统计学的角度预测变量未来发展情况。采用该技术对西安白蛇峪大桥应变监测数据进行分析,试验结果显示出较高的预测精度;采用该技术对重庆偏岩子大桥的应变、挠度、裂缝、倾斜监测量进行预测,试验统计结果表明,ARMA单步预测误差小于10%的置信度在97%以上,在工程实际中具有可实用性,可为桥梁结构的安全预警提供重要参考依据。     

RBF神经网络在公交行程时间预测中的应用

为提高公交行程时间预测结果准确度、减少预测过程花费时间,提出基于RBF神经网络公交行程时间预测模型。综合分析公交车辆行程时间动静态影响因素后,将网络变量输入模型对行程时间进行预测,以重庆市462公交线路为例,对模型进行验证,对比BP网络预测结果,表明RBF模型在速度和精确度上优于BP网络,具有一定实际应用价值。