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Automated crack identification in structures using acoustic waveforms and deep learning 利用声波波形和深度学习自动识别结构中的裂纹
Pub Date : 2024-08-11 DOI: 10.1186/s43065-024-00102-2
Mohamed Barbosh, Liangfu Ge, Ayan Sadhu
Structural elements undergo multiple levels of damage at various locations due to environments and critical loading conditions. The level of damage and its location can be predicted using acoustic emission (AE) waveforms that are captured from the generation of inherent microcracks. Existing AE methods are reliant on the feature selection of the captured waveforms and may be subjective in nature. To automate this process, this paper proposes a deep-learning model to predict the damage severity and its expected location using AE waveforms. The model is based on a densely connected convolutional neural network (CNN) that offers superior feature extraction and minimal training data requirements. Time-domain AE waveforms are used as inputs of the proposed model to automate the process of predicting the severity of damage and identifying the expected location of the damage in structural elements. The proposed approach is validated using AE data collected from a concrete beam and a wooden beam and plate. The results show the capability of the proposed method for predicting the level of damage with an accuracy range of 92-95% and identifying the approximate location of damage with 90-100% accuracy. Thus, the proposed method serves as a robust technique for damage severity prediction and localization in civil structures.
由于环境和关键载荷条件的影响,结构元件在不同位置会发生多级损坏。可以利用从固有微裂缝产生过程中捕获的声发射(AE)波形来预测损伤程度及其位置。现有的 AE 方法依赖于对捕获波形的特征选择,可能具有主观性。为了使这一过程自动化,本文提出了一种深度学习模型,利用 AE 波形预测损坏严重程度及其预期位置。该模型基于密集连接的卷积神经网络 (CNN),具有卓越的特征提取能力和最低的训练数据要求。时域 AE 波形用作拟议模型的输入,以自动预测损坏严重程度并确定结构元件中损坏的预期位置。利用从混凝土梁和木梁及木板上收集的 AE 数据对所提出的方法进行了验证。结果表明,所提方法预测损坏程度的准确率为 92-95%,识别损坏大致位置的准确率为 90-100%。因此,所提出的方法是一种用于民用结构损坏严重程度预测和定位的稳健技术。
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引用次数: 0
Inspection prioritization of gravity sanitary sewer systems using supervised machine learning algorithms 利用有监督的机器学习算法确定重力式卫生下水道系统检查的优先次序
Pub Date : 2024-07-29 DOI: 10.1186/s43065-024-00101-3
Karthikeyan Loganathan, Mohammad Najafi, Sharareh Kermanshachi, Praveen Kumar Maduri, Apurva Pamidimukkala
Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care.
地下污水收集系统会随着时间的推移而退化,因此公用事业所有者必须对其资产管理框架进行持续评估和改进,以保持其资产的性能。下水管道的检查和状况评估对于下水道系统的有效运行和维护至关重要。美国经常使用闭路电视(CCTV)来检查下水管道。由于大都市的下水管道数量众多,这一程序既昂贵又费力。对于需要维修或维护的卫生污水管道,可以根据其过去的表现提前确定检查的优先次序。因此,本研究的目的是利用从美国中南部地区一个城市收集到的数据,构建一个卫生污水管道状态预测模型。本研究的主要贡献在于采用了多类分类法并预测了管道的 PACP 分数。使用广泛使用的监督机器学习算法开发了状态预测模型,包括逻辑回归 (LR)、k-近邻 (k-NN) 和随机森林 (RF)。然而,大部分构建的模型都是通过有限的评估指标进行评估的,如接收器运算特性曲线(ROC)和曲线下面积(AUC)值。本文认为,对这些模型预测能力的评估不能仅依赖于 ROC 和 AUC 值。在本研究评估的三个模型中,LR 模型的 AUC 值为 0.76。然而,与其他模型相比,该模型的误分类或预测不准确的次数较多。因此,对这些模型进行了额外的评估,包括精确度、召回率和 F-1 分数(代表精确度和召回率的调和平均值)。奇怪的是,在 0 到 1 的范围内,LR 模型的 F1 分数为 0.28,而 RF 模型的 F1 分数为 0.45,AUC 值为 0.86。资产管理者在检查阶段使用现有模型评估卫生下水道状况并识别需要立即处理的重要下水道之前,可以对其进行改进。
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引用次数: 0
Numerical investigation on the deformation of railway embankment under normal faulting 正常断层作用下铁路路堤变形的数值研究
Pub Date : 2024-07-15 DOI: 10.1186/s43065-024-00100-4
Haohua Chen, Jiankun Liu, Zhijian Li, Xiaoqiang Liu, Jiyun Nan, Jingyu Liu
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引用次数: 0
Evaluation of the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to ultrasonic measurements 利用应用于超声波测量的孔弹性声学模型反演技术评估受腐蚀钢筋混凝土的物理特性
Pub Date : 2024-05-31 DOI: 10.1186/s43065-024-00099-8
Pierre-Philippe Beaujean, Samuel R. Shaffer, Francisco Presuel-Moreno, Matthew Brogden
The use of reinforced concrete is foundational to modern infrastructure. Acknowledging this, it is imperative that health monitoring techniques be in place to study corrosion within these structures. By using a non-destructive method for detecting the early formation of cracks within reinforced concrete, the method presented in this paper seeks to improve upon traditional techniques of monitoring corrosion, within reinforced concrete structures. In this paper, the authors present a method to evaluate the physical characteristics of reinforced concrete subject to corrosion using a poro-elastic acoustic model inversion technique applied to a set of ultrasonic measurements, which constitutes a novel approach to the problem of observing the impact of corroding rebars and resulting concrete damage. A non-contact ultrasonic transducer is operated at a carrier frequency of 500 [kHz], with a layer of saltwater separating the sensor from the concrete surface. Following this non-contact measurement collection of the surface and rebar echo responses, a poro-elastic model is used to model the sound propagation, through an adapted version of the Biot-Stoll model. At first, a set of default parameters, obtained from the physical characteristics of the reinforced concrete, are used to match experimental and simulated acoustic signature of the sample. Performing statistical averaging along the corroding rebar within three samples over a period of nearly nine months, a small but monotonous increase in the distance between the concrete surface and the top of the rebar, indicating gradual corrosion of the rebar. Next, a non-linear optimization algorithm is used to optimize the match between measured and simulated echoes. Through the implementation of this model parameter optimization, the root mean square error between measured and simulated responses was reduced by 63.7% for the full signal, and 62.6% for the rebar echo.
钢筋混凝土的使用是现代基础设施的基础。有鉴于此,必须采用健康监测技术来研究这些结构内部的腐蚀情况。通过使用非破坏性方法检测钢筋混凝土内部裂缝的早期形成,本文介绍的方法旨在改进钢筋混凝土结构内部腐蚀监测的传统技术。在本文中,作者介绍了一种评估受腐蚀的钢筋混凝土物理特性的方法,该方法采用了一种孔弹性声学模型反演技术,并将其应用于一组超声波测量,这是一种新颖的方法,可用于观测腐蚀钢筋的影响以及由此造成的混凝土损坏。非接触式超声波传感器的载波频率为 500[kHz],传感器与混凝土表面之间有一层盐水隔开。在对表面和钢筋回声响应进行非接触式测量收集之后,通过改编版的 Biot-Stoll 模型,使用孔弹性模型对声音传播进行建模。首先,根据钢筋混凝土的物理特性获得一组默认参数,用于匹配样本的实验和模拟声学特征。在近九个月的时间里,对三个样本中被腐蚀的钢筋进行统计平均,发现混凝土表面与钢筋顶部之间的距离出现了微小但单调的增长,这表明钢筋在逐渐腐蚀。接下来,使用非线性优化算法来优化测量回波与模拟回波之间的匹配。通过对模型参数进行优化,测量和模拟回声之间的均方根误差在全信号和钢筋回声中分别减少了 63.7% 和 62.6%。
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引用次数: 0
An investigation of belief-free DRL and MCTS for inspection and maintenance planning 用于检查和维护规划的无信念 DRL 和 MCTS 研究
Pub Date : 2024-04-29 DOI: 10.1186/s43065-024-00098-9
Daniel Koutas, Elizabeth Bismut, Daniel Straub
We propose a novel Deep Reinforcement Learning (DRL) architecture for sequential decision processes under uncertainty, as encountered in inspection and maintenance (I &M) planning. Unlike other DRL algorithms for (I &M) planning, the proposed +RQN architecture dispenses with computing the belief state and directly handles erroneous observations instead. We apply the algorithm to a basic I &M planning problem for a one-component system subject to deterioration. In addition, we investigate the performance of Monte Carlo tree search for the I &M problem and compare it to the +RQN. The comparison includes a statistical analysis of the two methods’ resulting policies, as well as their visualization in the belief space.
我们提出了一种新颖的深度强化学习(DRL)架构,用于不确定情况下的顺序决策过程,如检查和维护(I &M)规划中遇到的情况。与其他用于(I &M)规划的 DRL 算法不同,所提出的 +RQN 架构无需计算信念状态,而是直接处理错误观测。我们将该算法应用于受劣化影响的单组件系统的基本 I &M 规划问题。此外,我们还研究了蒙特卡洛树搜索在 I & M 问题上的性能,并将其与 +RQN 进行了比较。比较内容包括对两种方法得出的策略进行统计分析,以及它们在信念空间中的可视化。
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引用次数: 0
Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons 提高基础设施的抗灾能力:基于机器学习的自动卡车排下的桥梁评级系数预测
Pub Date : 2024-04-07 DOI: 10.1186/s43065-024-00096-x
Mohamed T. Elshazli, Dina Hussein, Ganapati Bhat, Ahmed Abdel-Rahim, Ahmed Ibrahim
The operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, transportation safety, and the sustainability of infrastructure. Truck platooning is an emerging truck configuration that is expected to become operational in the future due to the rapid advancements in connected vehicle technology and autonomous driving assistance. The platooning configuration enables trucks to be connected with themselves and the surrounding infrastructure. This arrangement has shown to be a promising solution to improve the vehicles’ fuel efficiency, reduce carbon dioxide emission, reduce traffic congestion, and improve transportation service. However, platooning may accelerate the damage accumulation of pavement and bridge structures due to the formation of multiple load axles within each platoon since those structures were not designed for such loads. According to AASHTO, bridges are designed based on a notional live load model comprised of one or two trucks per lane in conjunction with or separate from an applied uniform load (AASHTO, LRFD 2022). This damage, if accumulated, its repair would require billions of dollars from the government and would impede the movement of both people and goods. The potential damage to infrastructure may arise due to various factors such as the number of trucks in a platoon, gap spacing between trucks, and the type of trucks. This research work includes a thorough parametric study with 295,200 computer simulations using SAP 2000. The goal was to evaluate the effect of different truck platooning configurations on the load rating of existing bridges. The obtained results served as the dataset for training various machine learning models, including Random Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). Results showed that Random Forest model performed the best, with the lowest prediction errors. The proposed machine learning model has shown its effectiveness in identifying optimal platooning configurations for bridge structures within the scope of the study.
自动驾驶和互联卡车(ACT)实施后,货运的运行特征将发生重大变化。这一变化将对货运机动性、运输安全性和基础设施的可持续性产生重大影响。由于互联汽车技术和自动辅助驾驶技术的快速发展,卡车排车是一种新兴的卡车配置,预计将在未来投入使用。排队配置使卡车能够与自己和周围的基础设施相连接。这种安排在提高车辆燃油效率、减少二氧化碳排放、缓解交通拥堵和改善运输服务方面是一种很有前景的解决方案。然而,由于排载可能会在每个排内形成多个负载轴,从而加速路面和桥梁结构的损坏累积,因为这些结构在设计时并没有考虑到这种负载。根据 AASHTO 的规定,桥梁的设计是基于一个名义活荷载模型,该模型由每条车道上的一辆或两辆卡车与外加均布荷载共同或单独构成(AASHTO,LRFD 2022)。这种损坏如果累积起来,其修复将需要政府投入数十亿美元,并将阻碍人员和货物的流动。对基础设施的潜在破坏可能是由各种因素造成的,例如一排卡车的数量、卡车之间的间距以及卡车的类型。这项研究工作包括一项全面的参数研究,使用 SAP 2000 进行了 295 200 次计算机模拟。目的是评估不同卡车排布配置对现有桥梁荷载等级的影响。获得的结果作为训练各种机器学习模型的数据集,包括随机树、随机森林、多层感知器(MLP)、支持向量回归(SVR)、K-近邻(KNN)和极端梯度提升(XGBoost)。结果表明,随机森林模型表现最佳,预测误差最小。所提出的机器学习模型在确定研究范围内桥梁结构的最佳排布配置方面显示出其有效性。
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引用次数: 0
Resilience and systems- A traffic flow case example 复原力与系统--一个交通流量案例
Pub Date : 2024-03-28 DOI: 10.1186/s43065-024-00097-w
Khalilullah Mayar, David G. Carmichael, Xuesong Shen
Resilience has increasingly become a crucial topic to the function of various real-world systems as our planet undergoes a rising trend of uncertainty and change due to natural, human and technological causes. Despite its ubiquitous use, the term resilience is poorly and often inconsistently used in various disciplines, hindering its universal understanding and application. This study applies the resilience system interpretation framework, which defines resilience irrespective of its disciplinary association, in the form of adaptation and adaptive systems, to two traffic flow systems. The system framework defines resilience as the ability of the system state and form to return to their initial or other suitable state or form through passive and active feedback structures. Both components of the system framework are demonstrated through practical simulation scenarios on the modified viscous Burgers’ equation and the LWR-Greenshields model equipped with an adaptive Extremum seeking control, respectively. This novel and systematic understanding of resilience will advance resilience analysis, design, and measurement processes in various real-world systems in a unified fashion and subsequently pave the way for resilience operationalization and its integration into industry standards. A novel system definition for resilience and its constituent elements in the form of adaption is presented. The system framework is subsequently applied to two simple traffic flow systems. Modified viscous Burgers’ equation and LWR-Greenshields model equipped with an adaptive Extremum seeking control demonstrate the passive and active feedback structures as the two tools for obtaining system resilience. This cross-disciplinary system framework offers the potential for a greater understanding of resilience, eliminates overlap, and paves the way toward resilience operationalization.
由于自然、人类和技术原因,我们的星球正经历着不断上升的不确定性和变化趋势,因此,复原力日益成为现实世界中各种系统功能的一个重要课题。尽管抗灾能力一词的使用无处不在,但它在各学科中的用法却不尽相同,经常出现不一致的情况,阻碍了对它的普遍理解和应用。本研究将复原力系统解释框架应用于两个交通流系统,该框架以适应和自适应系统的形式定义复原力,而不考虑其学科关联。该系统框架将复原力定义为系统状态和形式通过被动和主动反馈结构恢复到初始或其他合适状态或形式的能力。该系统框架的两个组成部分分别通过对改良粘性布尔格斯方程和配备自适应极值寻求控制的 LWR-Greenshields 模型的实际模拟场景进行了演示。这种对复原力的新颖而系统的理解,将以统一的方式推进各种现实世界系统中的复原力分析、设计和测量过程,并为复原力的可操作性及其与行业标准的整合铺平道路。本文提出了一个新颖的复原力系统定义及其适应形式的构成要素。系统框架随后被应用于两个简单的交通流系统。修正的粘性布尔格斯方程和配备自适应极值寻求控制的 LWR-Greenshields 模型证明了被动和主动反馈结构是获得系统弹性的两种工具。这种跨学科的系统框架为更好地理解复原力提供了可能,消除了重叠,并为复原力的可操作性铺平了道路。
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引用次数: 0
Modeling retroreflectivity degradation of pavement markings across the US with advanced machine learning algorithms 利用先进的机器学习算法模拟全美路面标线的逆反射退化情况
Pub Date : 2024-02-21 DOI: 10.1186/s43065-024-00094-z
Ipshit Ibne Idris, Momen Mousa, Marwa Hassan
Retroreflectivity is the primary metric that controls the visibility of pavement markings during nighttime and in adverse weather conditions. Maintaining the minimum level of retroreflectivity as specified by Federal Highway Administration (FHWA) is crucial to ensure safety for motorists. The key objective of this study was to develop robust retroreflectivity prediction models that can be used by transportation agencies to reliably predict the retroreflectivity of their pavement markings utilizing the initially measured retroreflectivity and other key project conditions. A total of 49,632 transverse skip retroreflectivity measurements of seven types of marking materials were retrieved from the eight most recent test decks covered under the National Transportation Product Evaluation Program (NTPEP). Decision Tree (DT) and Artificial Neural Network (ANN) algorithms were considered for developing performance prediction models to estimate retroreflectivity at different prediction horizons for up to three years. The models were trained with randomly selected 80% data points and tested with the remaining 20% data points. Sequential ANN models exhibited better performance with the testing data than the sequential DT models. The training and testing R2 ranges of the sequential ANN models were from 0.76 to 0.96 and 0.55 to 0.94, respectively, which were significantly higher than the R2 range (0.14 to 0.75) from the regression models proposed in past studies. Initial or predicted retroreflectivity, snowfall, and traffic were found to be the most important inputs to model predictions.
逆反射率是控制夜间和恶劣天气条件下路面标识可见度的主要指标。保持联邦公路管理局(FHWA)规定的最低逆反射率水平对于确保驾车者的安全至关重要。本研究的主要目标是开发出强大的逆反射率预测模型,供交通机构使用,以便利用最初测量的逆反射率和其他关键项目条件,可靠地预测其路面标线的逆反射率。我们从国家交通产品评估计划 (NTPEP) 涵盖的八个最新测试平台中检索了七种标线材料的 49,632 次横向跳线逆反射率测量结果。在开发性能预测模型时,考虑了决策树 (DT) 和人工神经网络 (ANN) 算法,以估算不同预测范围内长达三年的逆反射率。这些模型使用随机选择的 80% 的数据点进行训练,并使用剩余的 20% 的数据点进行测试。与顺序 DT 模型相比,顺序 ANN 模型在测试数据方面表现出更好的性能。顺序 ANN 模型的训练和测试 R2 范围分别为 0.76 至 0.96 和 0.55 至 0.94,明显高于过去研究中提出的回归模型的 R2 范围(0.14 至 0.75)。研究发现,初始或预测的逆反射率、降雪量和交通量是模型预测的最重要输入。
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引用次数: 0
Surface crack treatment of concrete via nano-modified microbial carbonate precipitation 通过纳米改性微生物碳酸盐沉淀处理混凝土表面裂缝
Pub Date : 2024-02-20 DOI: 10.1186/s43065-024-00095-y
Tao Li, Hanqing Yang, Xiaohui Yan, Maolin He, Haojie Gu, Liming Yu
As a new concrete crack patching technology, microbial self-healing slurries offer favourable characteristics including non-pollution, ecological sustainability and good compatibility with concrete. In this paper, a nano-sio2-modified microbial bacteria liquid, combined with sodium alginate and polyvinyl alcohol, was used to prepare a nano-modified microbial self-healing slurry. This slurry was used to coat concrete under negative pressure in order to verify its restoration effect, and the micromorphology of the resulting microbial mineralization products was observed. The results revealed that patching the concrete using the nano-modified microbial slurry significantly improved its permeability, and increased its carbonization resistance by three times in comparison with the control group. Through a combination of Scanning electron microscopy (SEM) and X-ray diffraction (XRD) observation, it was determined that the microbial mineralization reaction products were mainly calcite crystals, which, integrated with the nano-sio2, sodium alginate and polyvinyl alcohol at the microscopic level, filled the internal pores of concrete, thus improving its durability. • Surface crack treatment of concrete using a nano-modified microbial slurry was investigated. • Patching concrete using nano-microbial slurry clearly improved its chloride penetration. • The carbonization of the concrete was three times in comparision with the control group. • The main product of the microbial mineralization reaction was calcite crystal.
作为一种新的混凝土裂缝修补技术,微生物自愈合泥浆具有无污染、生态可持续性和与混凝土相容性好等优点。本文利用纳米二氧化硅改性微生物菌液,结合海藻酸钠和聚乙烯醇,制备了纳米改性微生物自愈合浆料。为了验证其修复效果,在负压条件下用这种浆液涂抹混凝土,并观察了所产生的微生物矿化产物的微观形态。结果表明,与对照组相比,使用纳米改性微生物泥浆修补混凝土可显著改善其渗透性,并将其抗碳化能力提高三倍。通过扫描电子显微镜(SEM)和 X 射线衍射(XRD)观察,确定微生物矿化反应产物主要是方解石晶体,这些晶体在微观层面上与纳米二氧化硅、海藻酸钠和聚乙烯醇结合,填充了混凝土内部孔隙,从而提高了混凝土的耐久性。- 研究了使用纳米改性微生物浆液处理混凝土表面裂缝的方法。- 使用纳米微生物泥浆修补混凝土明显改善了其氯化物渗透性。- 混凝土的碳化程度是对照组的三倍。- 微生物矿化反应的主要产物是方解石晶体。
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引用次数: 0
The mechanism of spontaneous corrugation on the snowy and icy roads produced by the moving vehicles in cold regions 寒冷地区行驶车辆在冰雪路面上产生自发波纹的机理
Pub Date : 2024-01-30 DOI: 10.1186/s43065-024-00093-0
Hao Zheng, Yu Cao, Chongqian Ma, Shunji Kanie
Traffic safety in cold regions is seriously affected by the snow and ice brought by the extreme climate. The snowy and icy road cannot provide enough friction for the safe operation of vehicles due to its smooth and uneven surface. In this research, we are going to focus on the uneven corrugation occurred on snowy and icy roads and to investigate the formation mechanism of this spontaneous corrugation which can seriously threaten the traffic safety. According to field observations, we found that the corrugation phenomenon generated by moving vehicles is a complicated thermal–mechanical coupled process. In order to simplify this complicated process, we are going to focus on the mechanical process of the formation of spontaneous corrugation only at this stage. Field observation by time-lapse cameras has been conducted to disclose its forming process directly. Then, we adopted sand as the material to reproduce the spontaneous corrugation in the laboratory which can eliminate the influence of the thermal process. By considering the compressibility and mobility of the surface material comprehensively, a numerical model has been successfully constructed for imitating the forming process of corrugation. Then based on this proposed numerical model, a preliminary discussion on the influence of natural frequency on the number of the corrugation has been conducted. The relationship between the natural frequency which is decided by the vehicle itself and the corrugation is promising to be utilized in optimizing the vehicle design to improve the performance on the snowy and icy roads.
极端气候带来的冰雪严重影响了寒冷地区的交通安全。由于冰雪路面光滑不平,无法为车辆的安全行驶提供足够的摩擦力。在本研究中,我们将重点关注冰雪路面上出现的不平整波纹,并研究这种会严重威胁交通安全的自发波纹的形成机理。根据实地观察,我们发现车辆行驶时产生的波纹现象是一个复杂的热力-机械耦合过程。为了简化这一复杂过程,现阶段我们将只关注自发波纹形成的机械过程。通过延时摄影机进行实地观察,直接揭示其形成过程。然后,我们采用沙子作为材料,在实验室中再现自发波纹,这样可以消除热过程的影响。通过综合考虑表面材料的可压缩性和流动性,我们成功地构建了一个模仿波纹形成过程的数值模型。在此基础上,初步探讨了固有频率对波纹数量的影响。由车辆本身决定的固有频率与波纹之间的关系有望用于优化车辆设计,以提高车辆在冰雪路面上的性能。
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引用次数: 0
期刊
Journal of infrastructure preservation and resilience
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