适于TBM施工的HSP法实时预报技术设计与实现

为实现TBM施工隧道的实时地质预报,以TBM刀盘滚刀破岩震动作为震源的HSP法地质预报技术为基础,通过分析TBM施工工艺与机身结构特点,对HSP系统硬件和软件进行优化设计,使其小型化、自动化和智能化,并搭载于TBM上进行智能控制。在TBM掘进过程中,首先,通过隧道轮廓位置的检波器连续或高频次地采集地震反射波信号; 然后,经系统软件对数据的自动处理,实时获取掌子面前方地层反射特征参数图谱; 其次,通过智能识别技术完成对不良地质反射界面的有效拾取与判识; 最后,实现不良地质的探测。通过对适于TBM施工的HSP法实时预报技术进行优化设计,实时探查前方地层不良地质(体)位置、规模与性质,为TBM的高效施工起到一定的推助作用。     

Predictive power of inspection outcomes for future shipping accidents – an empirical appraisal with special attention for human factor aspects

This paper investigates whether deficiencies detected during port state control (PSC) inspections have predictive power for future accident risk, in addition to other vessel-specific risk factors like ship type, age, size, flag, and owner. The empirical analysis links accidents to past inspection outcomes and is based on data from all around the globe of PSC regimes using harmonized deficiency codes. These codes are aggregated into eight groups related to human factor aspects like crew qualifications, working and living conditions, and fatigue and safety management. This information is integrated by principal components into a single overall deficiency index, which is related to future accident risk by means of logit models. The factor by which accident risk increases for vessels with above average compared to below average deficiency scores is about 6 for total loss, 2 for very serious, 1.5 for serious, and 1.3 for less-serious accidents. Relations between deficiency scores and accident risk are presented in graphical format. The results may be of interest to PSC authorities for targeting inspection areas, to maritime administrations for improving asset allocation based on prediction scenarios connected with vessel traffic data, and to maritime insurers for refining their premium strategies.     

基于三点模型的柴油机热工参数预测

准确预测热工参数劣化趋势是对柴油机实施预测维修的前提。已有预测技术均视不同时段信息所具有的建模价值相等,忽略了近期信息比远期信息更能反映设备当前运行态势这一客观事实,导致预测模型难以准确描述柴油机热工参数的真实变化规律。针对该问题,提出一种基于三点模型的柴油机热工参数预测方法,并采用新陈代谢法同步更新建模数据,以提高建模数据的利用效率及预测精度。与灰色系统GM(1,1)模型预测精度的对比结果表明,三点模型能够有效处理不同时段信息在反映设备运行态势能力上的差异,得到更为准确的预测结果。     

基于HMM-SVR的船舶动力设备故障模式识别与状态预测研究

船舶动力设备因故障监测信号样本少、变化缓慢且数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。鉴于此,文章研究了基于隐马尔科夫模型的故障模式识别方法,利用该模型将微弱变化的信号特征转换为变化较大的对数似然概率对故障模式实现有效识别。在此基础上进一步提出基于HMM-SVR的设备状态预测模型,将遗传算法用于支持向量回归模型参数寻优,并结合隐马尔科夫模型,实现对设备状态的预测。对船用柴油机进行仿真,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备的当前状态。     

安全水平法在SOLAS 救生艇结构强度衡准制定中的应用

本文将安全水平法应用于SOLAS第III章救生艇结构强度衡准的制定。主要采用综合安全评估对救生艇结构强度相关的功能性要求进行衡准的初步制定,从而得出需要的安全水平。基于强度衡准理论分析、实艇试验和事故数据分析相结合的方式评估了现有救生艇结构强度的安全水平,对衡准的合理性提出修改的建议。研究结果为安全水平法在船舶技术标准制定和安全水平评估方面的应用提供了实际案例和使用建议。     

Predicting queue variability to enable analysis of overload risk

ABSTRACT

Predicting the risk of traffic demands and delays exceeding critical limits at road junctions, airports, hospitals, etc., requires knowing how both mean and variance of queue size vary over time. Microscopic simulation can explore variability but is computationally demanding and gives only sample results. A computationally efficient approximation to the mean is used in many modelling tools, but only empirical extensions for variance in particular situations have been available. The paper derives theoretical formulae for time-dependent and equilibrium variance, believed to be novel and to apply generally to queues covered by the Pollaczek–Khinchin mean formula, and offering possible structural insights. These are applied in an extended approximation giving mutually consistent mean and variance estimates with improved accuracy. Tests on oversaturated peak demand cases are compared with Markov probabilistic simulation, demonstrating accuracy (R^2?>?0.99) for typical random, priority-like (M/M/1) and traffic-signal-like (M/D/1) queues. Implications for risk analysis, planning and policy are considered.     

Risk assessment framework for exposure of cargo and ports to natural hazards and climate extremes

There is an increase in risks and catastrophic losses in maritime transport including ports and cargo. Significant losses have been associated with large scale natural hazards, such as earthquakes, tsunami, cyclones, and other extreme weather events. This paper identifies the main gaps in understanding maritime risks in transportation research. The gaps are attributed to insufficient empirical work available from the maritime transport and logistics research community to guide multi-risk and natural hazards impact assessment on seaport and cargo. In addition, disaster studies communities have barely made adequate efforts to understand and assess port and cargo risks arising from multi-hazards and disaster events. This paper examines existing conceptual frameworks concerning exposure and risk assessments of natural catastrophe’s impacts. Furthermore, the paper identifies trends and gaps in risk assessment frameworks in the field of disaster studies that can be beneficial for maritime risk research. The authors propose a new risk assessment framework that can guide future research and multi-hazard risk assessment processes at different scales of maritime risks.     

基于AIGA-WLSSVM的埋地管道腐蚀速率预测方法

为了降低埋地管道腐蚀影响因素之间的复杂相关性,提高腐蚀预测精度,文中提出一种基于自适应免疫遗传算法-加权最小二乘支持向量机(AIGA-WLSSVM)的埋地管道腐蚀速率预测建模方法,并采用AIGA优化模型参数,进一步提高模型的学习能力和稳定性。最后通过实例分析验证了AIGA-WLSSVM建模方法在埋地管道腐蚀速率预测中的可行性和有效性,为埋地管道的检修与更换提供参考。     

长江航道大型整治工程施工安全控制关键技术

长江航道大型整治工程施工河段长、工程量大且工序多,施工水域船舶通航密度大、船舶种类多,施工安全生产控制与河段通航安全面临巨大挑战。依托长江干线下、中、上游典型大型航道整治工程,围绕工程施工全过程风险管控及安全保障技术,采用理论分析、仿真模拟、现场试验、系统研发相结合的方法,提出了长江航道大型整治工程风险识别与评估方法,构建了通航安全与施工生产安全风险预测模型,研究了长江航道大型整治工程全方位、全过程、全要素施工安全保障与控制技术,并基于AIS和云技术开发了长江航道整治工程施工区安全综合信息平台,为保障大型航道整治工程生产及通航安全提供了理论依据和技术手段。