粘土水泥复合浆材强度研究

通过室内试验,研究了水灰比、龄期、粘土加量、压滤效应等对粘土水泥浆材结石无侧限抗压强度的影响。分析试验结果表明,粘土水泥浆无侧限抗压强度和浆液水灰比随着龄期的变化规律呈指数关系。在完全压滤作用下,抗压强度随着压力的增大而大幅提高,且强度随龄期呈二次多项式规律变化,如在0.8 MPa压力作用下,粘土水泥浆材结石28 d无侧限抗压强度增大3倍左右;抗压强度随着粘土加量的增大而减小,粘土加量在50%~60%间出现大幅下降。研究表明,在注浆工程中采用粘土水泥浆材时,应尽可能创造条件产生较充分的压滤效应,控制粘土加量不超过50%,水灰比在2∶1左右,可以获得足够的强度指标。适量添加粘土,这类浆材完全可以用于注浆加固工程中。     

基于FT2232H的高速数据采集系统设计

针对各种虚拟仪器对传输速率和开发难度的要求,设计了一种基于新型USB2．0高速接口的虚拟仪器采集系统。本系统采用FTDI公司第五代USB2．0接口芯片FT2232H,利用其异步FIFO接口与STM32F103的FSMC接口相互传输数据,使用LabView设计上位机界面,调用其提供的动态链接库DLL和MCU固件库,可快速实现高速接口的数据传输。     

华南地区典型土壤渗透性的改良配置研究

为改善我国华南地区土壤黏性大、渗透性差的问题,研究华南地区土壤与落地灰、砾石、沸石、海砂、草根和枯树叶等多种材料的改良配置,测试不同重量配比下试验土壤的渗透性,获取不同有机、无机材料对典型土壤自然入渗能力的改善效果(包括各种材料的单独作用和择优性混合作用效果)。结果表明:单材料试验中,低重量配比的草根和枯树叶试验土壤的峰值入渗速率分别为0.178 mL/s和0.163 mL/s,且有机材料的改良效果优于无机材料。高重量配比的5～9 mm砾石和5～9 mm落地灰试验土壤分别在重量配比为40%和60%情况下,峰值入渗速率达0.338 mL/s和0.717 mL/s,在同重量配比的所有材料里具有最佳入渗效果。多材料混合试验中,枯树叶、5～9 mm砾石和5～9 mm落地灰相结合的试验土壤实现最佳入渗效果。研究成果为改善华南地区土壤渗透性能提供科学依据。     

模糊马尔可夫随机场理论在阴影检测中的应用

阴影的检测是目标检测、目标跟踪、视频监控等领域的一个关键问题。提出了一种基于模糊马尔可夫随机场的阴影检测算法。该算法把阴影检测问题看做是一个求最优化的像素点分类问题。对于输入的视频,提取背景图像,找出阴影和前景目标物体区域。通过计算阴影概率分布,前景概率分布,隶属度函数,建立模糊马尔可夫随机场。应用贝叶斯准则,最大后验(MAP)估计和条件迭代模式(ICM)算法,寻找最优化的模糊马尔可夫随机场,并利用最大隶属度原则消除模糊性,得到阴影检测的结果。实验证明,文中算法具有较好的阴影检测率和目标检测率。     

基于改进3D UNet的前列腺MR图像分割

针对前列腺磁共振 (magnetic resonance, MR)图像边缘模糊、对比度较低,灰度值分布不均衡而导致分割精度较差的问题,提出了一种结合双路径注意力(dual path attention,DPA) 和多尺度特征聚合(multi-scale feature aggregation,MFA) 模块的改进3D UNet网络模型。首先,对数据集进行重采样和裁剪处理以适应模型输入。然后,在3D UNet网络的编码器各层引入DPA 并添加残差连接,加强特征的 编码能力。同时,在网络解码器中加入MFA模块,以充分利用空间上下文信息,增强语义信息。最后,在公开数据集PROMISE12上进行验证,所提出的模型的Dice系数为89.90%,Hausdorff 距离为9.37 mm。相比较于其他模型,所提出模型的分割结果更优,且参数量和运算量更少。     

UAV imagery based potential safety hazard evaluation for high-speed railroad using Real-time instance segmentation

Potential safety hazards (PSHs) along the track needs to be inspected and evaluated regularly to ensure a safe environment for high-speed railroad operations. Other than track inspection, evaluating potential safety hazards in the nearby areas often requires inspectors to patrol along the track and visually identify potential threads to the train operation. The current visual inspection approach is very time-consuming and may raise safety concerns for the inspectors, especially in remote areas. Using the unmanned aerial vehicle (UAV) has great potential to complement the visual inspection by providing a better view from the top and ease the safety concerns in many cases. This study develops an automatic PSH detection framework named YOLARC (You Only Look at Railroad Coefficients) using UAV imagery for high-speed railroad monitoring. First, YOLARC is equipped with a new backbone having multiple available receptive fields to strengthen the multi-scale representation capability at a granular level and enrich the semantic information in the feature space. Then, the system integrates the abundant semantic features at different high-level layers by a light weighted feature pyramid network (FPN) with multi-scale pyramidal architecture and a Protonet with residual structure to precisely predict the track areas and PSHs. A hazard level evaluation (HLE) method, which calculates the distance between identified PSH and the track, is also developed and integrated for quantifying the hazard level. Experiments conducted on the UAV imagery of high-speed railroad dataset show the proposed system can quickly and effectively turn UAV images into useful information with a high detection rate and processing speed.     

Stakeholder requirement evaluation of smart industrial service ecosystem under Pythagorean fuzzy environment for complex industrial contexts: A case study of renewable energy park

This study focuses on ways to systematically evaluate stakeholder requirements when developing a smart industrial service ecosystem (SISE) in a complex industrial context. The SISE development requires considering the service requirement from both the complex industrial context and service ecosystem manners. This study proposes a systematic framework for stakeholder requirement evaluation in SISE. The first part of the framework is the industrial context-viable system model with ecological thinking (IC-VESM) to elicit the service requirements for the SISE, which facilitates a systematic analysis of the service value proposition and service requirement elicitation in the operational lifecycle of an entire industrial context. This second part of the framework proposes a method for evaluating service requirements that is both feasible and systematic. This is achieved by combining the Fuzzy Kano and AHP methods in a Pythagorean fuzzy (PF) environment. The PF Kano computes the categories and determines the weights of service requirements from a consumer perspective, while the PF AHP hierarchically analyzes the service requirements and provides pairwise comparison paths for design experts. Finally, an illustrative case study in a renewable energy context was used to demonstrate the feasibility and effectiveness of the methodology. The proposed theoretical model provides more reliable and systematic outcomes than traditional methods when eliciting service requirements and evaluating complex smart industrial service solutions. The study has practical implications by providing useful insights for companies to recognize key smart service requirements in complex industrial contexts and to improve sustainable development.     

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.     

Spatial-temporal analysis of safety risks in trajectories of construction workers based on complex network theory

Understanding the traffic patterns of construction workers on high-risk construction sites is important for implementing behaviour-based safety management. However, safety risks in worker trajectories are a complex system with high uncertainty. This resulted in few studies focusing on analysing the spatial–temporal risk in workers' trajectories from a systematic perspective. This study designs a new framework to explore the spatial–temporal patterns of safety risks in the trajectories of construction workers based on complex network theory. First, an integrated site safety risk classification method by combining hazard sources and group trajectory distribution is developed to accurately describe the spatial distribution of site risks. Second, a new complex network chronnet is used to construct complex networks containing risk information for spatial–temporal analysis. Finally, key risk areas and risk transition patterns are identified through the analysis of network measures. The study also developed a computational program that allows the network to be constructed and analysed in real-time. The feasibility and effectiveness of the method are verified through a case study. The results show that the method can reveal the risk distribution at the micro level, and explore the risk clustering and transition features in worker trajectories at the macro level. The study allows for an accurate analysis of dynamic risk patterns in construction workers' trajectories from a systematic perspective. It can also provide theoretical and practical support for personalized and adaptive behaviour-based safety management for construction workers.     

Physics-informed few-shot learning for wind pressure prediction of low-rise buildings

This research proposes a physics-informed few-shot learning model to predict the wind pressures on full-scale specimens based on scaled wind tunnel experiments. Existing machine learning approaches in the wind engineering domain are incapable of accurately extrapolating the prediction from scaled data to full-scale data. The model presented in this research, on the other hand, is capable of extrapolating prediction from large-scale or small-scale models to full-scale measurements. The proposed ML model combines a few-shot learning model with the existing physical knowledges in the design standards related to the zonal information. This physical information helps in clustering the few-shot learning model and improves prediction performance. Using the proposed techniques, the scaling issue observed in wind tunnel tests can be partially resolved. A low mean-squared error, mean absolute error, and a high coefficient of determination were observed when predicting the mean and standard deviation wind pressure coefficients of the full-scale dataset. In addition, the benefit of incorporating physical knowledge is verified by comparing the results with a baseline few-shot learning model. This method is the first of its type as it is the first time to extrapolate in wind performance prediction by combining prior physical knowledge with a few-shot learning model in the field of wind engineering. With the benefit of the few-shot learning model, only a low-resolution of the measuring tap configuration is required, and the reliance on physical wind tunnel experiments can be reduced. The physics-informed few-shot learning model is an efficient, robust, and accurate alternate solution to predicting wind pressures on full-scale structures based on various modeled scale experiments.