基于数据驱动的隧道地质信息精细化管理系统研发与应用

针对现阶段隧道超前地质预报系统管理粗放、数据利用率低等问题,结合数据驱动原理重塑业务逻辑,研发新的隧道地质信息系统TGIS。通过高度集成隧道勘察设计阶段地质、施工阶段超前预报地质及掌子面开挖揭示地质,实现基于数据驱动的预报方法优选、临近预报提醒、分级实时预警与有效处置、掌子面自动素描、预报成果辅助判识等功能,达到隧道地质信息精细化管理的目的。经现场使用表明： 基于数据驱动的隧道地质信息精细化管理系统界面友好、运行稳定、效果良好,各项功能达到设计要求,相对于传统超前地质预报系统具有明显的优势,可普遍服务于隧道建设。     

基于深度学习的破碎带盾构施工沉降预测分析

为进一步提高复杂地层条件下盾构沉降预测的准确性,以广州地铁7号线1期工程谢村站-钟村站区间盾构工程为依托,针对破碎带盾构隧道沉降控制难题,提出基于深度学习的人工智能预测模型。通过分析开挖面破碎带分布规律,确定将破碎带面积比作为地层特性参数。采用相关系数矩阵分析不同施工参数与破碎带面积比的相关性,确定采用刀盘转矩代表破碎带面积比实时描述地层分布特性。以刀盘转矩、盾尾间隙与注浆量作为输入值,地面沉降作为输出值训练深度学习模型,并利用训练后的深度学习模型进行沉降预测分析。通过分析预测结果与沉降实测值的对比验证预测模型的有效性。     

R/S分析法在盾构下穿机场过程中的地表变形规律研究

为保证机场的正常运营,在盾构下穿机场的施工过程中,对地表沉降变形控制的要求较高,因此对穿越过程中的施工控制和变形规律研究具有重要意义。首先,基于盾构下穿机场的工程条件,分析盾构下穿过程中的风险,并提出相应的控制措施;同时,根据现场监测数据,分析盾构下穿机场过程中的地表沉降变形规律,并利用R/S分析法对地表沉降的发展趋势进行研究。结果表明:盾构施工引起的机场地表变形均在控制值范围以内,且各监测点在不同序列条件下的Hurst指数均大于0.5,具有沉降持续减弱的趋势,验证下穿过程中风险控制措施的有效性,为类似下穿机场的盾构施工提供一定的实践经验,也为盾构施工引起的地表沉降变形规律研究提供参考。     

Strain prediction for critical positions of FPSO under different loading of stored oil using GAIFOA-BP neural network

FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.     

小管径垂直管水下排气完全携带点实验及分析

船用柴油机采用水下排气方式具有降低排气噪声、改善舱外空气质量等优点,在大型豪华游轮等特殊船舶中具有广泛应用前景。为防止海水由排气管倒灌入柴油机造成事故,需弄清水下排气管内水倒流发生的条件。对大管径垂直管防止水倒灌临界条件即完全携带点的已有认识较为统一,而对小管径垂直管仍未有清晰的认识。在管径25~100 mm范围对垂直管的完全携带点进行了实验,探究了小管径垂直管的完全携带点预报方法。实验结果表明在小管径范围,垂直管完全携带点对应的临界表观气速随管径增大而升高,但气相Wallis数随管径增大而减小,说明Wallis数过度关联了管径对临界表观气速的影响。基于气相Kutateladze数和无量纲气相黏性,对小管径垂直管提出了新的完全携带点预报模型和相应实验关联式。     

STAAD/CHINA在搭架设计及强度校核中的应用

搭架作业是船舶修造日常工作中比较普通、常见的工程,合理、快速地设计和校验脚手架的强度,直接影响生产施工的安全性、经济性和效率。文章介绍了如何使用有限元软件STAAD/CHINA对搭架方案进行建模计算及校验,计算结果接近实际,提高了强度校验的效率。     

基于ANN的船舶撞击高桩码头群桩损伤位置预测

本文采用有限元软件ABAQUS建立了船舶撞击高桩码头群桩的空间有限元模型。通过计算评估了撞击力、桩体刚度、撞击位置和撞击角度下对群桩结构损伤位置的影响。基于人工神经网络(ANN)方法,对不同参数组合下的群桩结构损伤位置进行了预测,并对ANN方法的可行性进行了评估。     

基于有向图卷积神经网络的交通预测与拥堵管控

为解决城市快速路正面临的日益严重的交通拥堵问题,提出了一种针对城市快速路的基于有向图卷积神经网络的交通预测与拥堵管控方法,该方法能够有效利用海量交通数据进行交通预测,实现拥堵的主动管控.首先,基于交通路网的空间有向性和交通流的时空特性,定义了有向的距离影响矩阵、修正欧式距离矩阵和自由流可达矩阵,构建出有向的图卷积算子,...     

Tuning of transfer functions for analysis of wave–ship interactions

Transfer functions are often used together with a wave spectrum for analysis of wave–ship interactions, where one application addresses the prediction of wave-induced motions or other types of global responses. This paper presents a simple and practical method which can be used to tune the transfer function of such responses to facilitate improved prediction capability. The input to the method consists of a measured response, i.e. time series sequences from a given sensor, the 2D wave spectrum characterising the seaway in which the measurements are taken, and an initial estimate of the transfer function for the response in study. The paper presents results obtained using data from an in-service container ship. The 2D wave spectra are taken from the ERA5 database, while the transfer function is computed by a simple closed-form expression. The paper shows that the application of the tuned transfer function leads to predictions which are significantly improved compared to using the transfer function without tuning.     

基于支持向量机回归树的内燃机振动信号趋势预测

针对标准回归树建立在统计分析基础上所存在的缺陷,提出一种基于支持向量机的回归树预测模型。首先,根据原始振动信号趋势序列构建回归树;然后,针对回归树上包含样本数过少的节点,利用支持向量机,建立能够反映重要变量与响应变量之间映射关系的回归模型。仿真结果表明:即便由于设备出现异常,导致振动信号趋势序列出现非平稳、突变情况,该方法也能准确地预测,性能优于标准分析方法,具有一定的工程实用性。