Exploring the Applications of Digital Twin Technology in Enhancing Sustainability in Civil Engineering:A Review

With the advent of the big data era and the rise of Industrial Revolution 4.0,digital twins(DTs)have gained sig-nificant attention in various industries.DTs offer the opportunity to combine the physical and digital worlds and aid the digital transformation of the civil engineering industry.In this paper,605 documents obtained from the search werefirst analysed using CiteSpace for literature visualisation,and an author co-occurrence network,a keyword co-occurrence network,and a keyword clustering set were obtained.Next,through a literature review of 86 papers,this paper summarises the current status of DT application in civil engineering based on a review of the origins,concepts,and implementation techniques of DTs,and it introduces the application of DTs in the full project lifecycle.This study shows that DTs have great potential to address many of the challenges faced by civil engineering.In this regard,the paper also presents some thoughts on the future directions of DT research.

Analyzing topics in social media for improving digital twinning based product development

Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product development.Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital spaces.This work mines real-world consumer feedbacks through social media topics,which is significant to product development.We specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a product.The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution.Therefore,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics.To this end,this work combines deep learning and survival analysis to predict the prevalent time of topics.We propose a specialized deep survival model which consists of two modules.The first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network structure.Moreover,a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction.Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

Data-Centric Approach to Digital Twin Modeling of Production Lines

Digital twin(DT) is a virtual replica of a physical world that has become one of the most important ideas in the manufacturing industry’s digital revolution. DT modeling is a vital issue in building a DT of a production line. In this paper, a method is proposed to address the difficulties of complicated production line business and data heterogeneity. The method focuses on essential data in the production line and creates conceptual and information models based on the ArtiFlow model and AutomationML(AML). Conceptual models are mainly used to describe and analyze the business activities of the production line, and information models describe real production lines in the form of XML files. The proposed modeling approach has been applied to a real-world clothing production line to demonstrate its feasibility and effectiveness.

Smart Design and Manufacturing the Welded Q350 Steel Frames via Lifecycle Management Strategy of Digital Twin

Artificial intelligent aided design and manufacturing have been recognized as one kind of robust data-driven and data-intensive technologies in the integrated computational material engi-neering(ICME)era.Motivated by the dramatical developments of the services of China Railway High-speed series for more than a decade,it is essential to reveal the foundations of lifecycle man-agement of those trains under environmental conditions.Here,the smart design and manufacturing of welded Q350 steel frames of CR200J series are introduced,presenting the capability and opportu-nity of ICME in weight reduction and lifecycle management at a cost-effective approach.In order to address the required fatigue life time enduring more than 9×10^(6)km,the response of optimized frames to the static and the dynamic loads are comprehensively investigated.It is highlighted that the maximum residual stress of the optimized welded frame is reduced to 69 MPa from 477 MPa of previous existing one.Based on the measured stress and acceleration from the railways,the fatigue life of modified frame under various loading modes could fulfil the requirements of the lifecycle man-agement.Moreover,our recent developed intelligent quality control strategy of welding process mediated by machine learning is also introduced,envisioning its application in the intelligent weld-ing.

Modeling and Analysis of Production Logistics Spatio-Temporal Graph Network Driven by Digital Twin

In the process of logistics distribution of manufacturing enterprises, the automatic scheduling method based on the algorithm model has the advantages of accurate calculation and stable operation, but it excessively relies on the results of data calculation, ignores historical information and empirical data in the solving process, and has the bottleneck of low processing dimension and small processing scale. Therefore, in the digital twin(DT) system based on virtual and real fusion, a modeling and analysis method of production logistics spatio-temporal graph network model is proposed, considering the characteristics of road network topology and time-varying data. In the DT system, the temporal graph network model of the production logistics task is established and combined with the network topology, and the historical scheduling information about logistics elements is stored in the nodes. When the dynamic task arrives, a multi-stage links probability prediction method is adopted to predict the possibility of loading, driving, and other link relationships between task-related entity nodes at each stage. Several experiments are carried out, and the prediction accuracy of the digital twin-based temporal graph network(DTGN) model trained by historical scheduling information reaches 99.2% when the appropriate batch size is selected. Through logistics simulation experiments, the feasibility and the effectiveness of production logistics spatio-temporal graph network analysis methods based on historical scheduling information are verified.

Digital Twin-based Quality Management Method for the Assembly Process of Aerospace Products with the Grey-Markov Model and Apriori Algorithm

The assembly process of aerospace products such as satellites and rockets has the characteristics of single-or small-batch production,a long development period,high reliability,and frequent disturbances.How to predict and avoid quality abnormalities,quickly locate their causes,and improve product assembly quality and efficiency are urgent engineering issues.As the core technology to realize the integration of virtual and physical space,digital twin(DT)technology can make full use of the low cost,high efficiency,and predictable advantages of digital space to provide a feasible solution to such problems.Hence,a quality management method for the assembly process of aerospace products based on DT is proposed.Given that traditional quality control methods for the assembly process of aerospace products are mostly post-inspection,the Grey-Markov model and T-K control chart are used with a small sample of assembly quality data to predict the value of quality data and the status of an assembly system.The Apriori algorithm is applied to mine the strong association rules related to quality data anomalies and uncontrolled assembly systems so as to solve the issue that the causes of abnormal quality are complicated and difficult to trace.The implementation of the proposed approach is described,taking the collected centroid data of an aerospace product’s cabin,one of the key quality data in the assembly process of aerospace products,as an example.A DT-based quality management system for the assembly process of aerospace products is developed,which can effectively improve the efficiency of quality management for the assembly process of aerospace products and reduce quality abnormalities.

Progress and prospects of oil and gas production engineering technology in China

This paper summarizes the important progress in the field of oil and gas production engineering during the"Thirteenth Five-Year Plan"period of China,analyzes the challenges faced by the current oil and gas production engineering in terms of technological adaptability,digital construction,energy-saving and emission reduction,and points out the future development direction.During the"Thirteenth Five-Year Plan"period,series of important progresses have been made in five major technologies,including separated-layer injection,artificial lift,reservoir stimulation,gas well de-watering,and workover,which provide key technical support for continuous potential tapping of mature oilfields and profitable production of new oilfields.Under the current complex international political and economic situation,oil and gas production engineering is facing severe challenges in three aspects:technical difficulty increases in oil and gas production,insignificant improvements in digital transformation,and lack of core technical support for energy-saving and emission reduction.This paper establishes three major strategic directions and implementation paths,including oil stabilization and gas enhancement,digital transformation,and green and low-carbon development.Five key research areas are listed including fine separated-layer injection technology,high efficiency artificial lift technology,fine reservoir stimulation technology,long term gas well de-watering technology and intelligent workover technology,so as to provide engineering technical support for the transformation,upgrading and high-quality development of China’s oil and gas industry.

Digital Twins for Engineering Asset Management:Synthesis,Analytical Framework,and Future Directions

Effective engineering asset management(EAM)is critical to economic development and improving livability in society,but its complexity often impedes optimal asset functionalities.Digital twins(DTs)could revolutionize the EAM paradigm by bidirectionally linking the physical and digital worlds in real time.There is great industrial and academic interest in DTs for EAM.However,previous review studies have predominately focused on technical aspects using limited life-cycle perspectives,failing to holistically synthesize DTs for EAM from the managerial point of view.Based on a systematic literature review,we introduce an analytical framework for describing DTs for EAM,which encompasses three levels:DT 1.0 for technical EAM,DT 2.0 for technical-human EAM,and DT 3.0 for technical-environmental EAM.Using this framework,we identify what is known,what is unknown,and future directions at each level.DT 1.0 addresses issues of asset quality,progress,and cost management,generating technical value.It lacks multi-objective self-adaptive EAM,however,and suffers from high application cost.It is imperative to enable closed-loop EAM in order to provide various functional services with affordable DT 1.0.DT 2.0 accommodates issues of human-machine symbiosis,safety,and flexibility management,generating managerial value beyond the technical performance improvement of engineering assets.However,DT 2.0 currently lacks the automation and security of human-machine interactions and the managerial value related to humans is not prominent enough.Future research needs to align technical and managerial value with highly automated and secure DT 2.0.DT 3.0 covers issues of participatory governance,organization management,sustainable development,and resilience enhancement,generating macro social value.Yet it suffers from organizational fragmentation and can only address limited social governance issues.Numerous research opportunities exist to coordinate different stakeholders.Similarly,future research opportunities exist to develop DT 3.0 in a more open and complex system.

A systematic review of digital twins for electric vehicles

The transport sector emits 18%of global CO_(2).Industry and consumers must adopt green mobility to reduce emissions and climate change.This will help achieve sustainability by improving efficiency and reducing greenhouse gas emissions.Thus,smart electric vehicles(SEVs)have emerged.Digital twins concept and technology may help launch SEVs to the market by analysing and optimising supporting infrastructure.This work aims to fill in the gaps between different pieces of research by giving a full review from a technical and scientifically neutral point of view.The study looks at how digital twin technology can be used in smart car systems by looking at its promise and the hurdles faced.Based on a comprehensive literature survey,this is the first in-depth look at how digital twin technology can be used in smart electric cars.The review has been organised into specific areas of the smart vehicle system,such as drive train system battery management system,driver assistance system,vehicle health monitoring system,vehicle power electronics.This review goes into detail about each component of the car to provide an overall view of the smart vehicle system as a whole.This review makes it easier to understand how digital twin technology can be utilized into each area from a scientific point of view.Lastly,the work looks at the technological and economic impact of digital twin technology,which will make considerable changes in car manufacturing processes,as well as help address current obstacles in utilizing advanced technologies.

Digital twin-driven green material optimal selection and evolution in product iterative design

In recent years,green concepts have been integrated into the product iterative design in the manufacturing field to address global competition and sustainability issues.However,previous efforts for green material optimal selection disregarded the interaction and fusion among physical entities,virtual models,and users,resulting in distortions and inaccuracies among user,physical entity,and virtual model such as inconsistency among the expected value,predicted simulation value,and actual performance value of evaluation indices.Therefore,this study proposes a digital twin-driven green material optimal selection and evolution method for product iterative design.Firstly,a novel framework is proposed.Subsequently,an analysis is carried out from six perspectives:the digital twin model construction for green material optimal selection,evolution mechanism of the digital twin model,multi-objective prediction and optimization,algorithm design,decision-making,and product function verification.Finally,taking the material selection of a shared bicycle frame as an example,the proposed method was verified by the prediction and iterative optimization of the carbon emission index.

Edge-cloud collaborative intelligent production scheduling based on digital twin

With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production scheduling problem in large-scale manufacturing environment,digital twin(DT)places high demand on data processing capability of the terminals.It requires both global prediction and real-time response abilities.In order to solve the above problem,a DT-based edge-cloud collaborative intelligent production scheduling(DTECCS)system was proposed,and the scheduling model and method were introduced.DT-based edge-cloud collaboration(ECC)can predict the production capacity of each workshop,reassemble customer orders,optimize the allocation of global manufacturing resources in the cloud,and carry out distributed scheduling on the edge-side to improve scheduling and tasks processing efficiency.In the production process,the DTECCS system adjusts scheduling strategies in real-time,responding to changes in production conditions and order fluctuations.Finally,simulation results show the effectiveness of DTECCS system.

Revisiting digital twins: Origins, fundamentals, and practices

The digital twins(DT)has quickly become a hot topic since it was proposed.It appears in all kinds of commercial propaganda and is widely quoted by academic circles.However,the term DT has misstatements and is misused in business and academics.This study revisits DT and defines it to be a more advanced system/product/service modeling and simulation environment that combines most modern information communication technologies(ICTs)and engineering mechanism digitization and characterized by system/product/service life cycle management,physically geometric visualization,real-time sensing and measurement of system operating conditions,predictability of system performance/safety/lifespan,and complete engineering mechanisms-based simulations.The idea of DT originates from modeling and simulation practices of engineering informatization,including virtual manufacturing(VM),model predictive control,and building information modeling(BIM).On the basis of the two-element VM model,we propose a three-element model to represent DT.DT does not have its unique technical characteristics.The existing practices of DT are extensions of the engineering informatization embracing modern ICTs.These insights clarify the origin of DT and its technical essentials.

Digital twin-driven complexity management in intelligent manufacturing[version 1;peer review:2 approved]

Complexity management is one of the most crucial and challenging issues in manufacturing.As an emerging technology,digital twin provides an innovative approach to manage complexity in a more autonomous,analytical and comprehensive manner.This paper proposes an innovative framework of digital twin-driven complexity management in intelligent manufacturing.The framework will cover three sources of manufacturing complexity,including product design,production lines and supply chains.Digital twin provides three services to manage complexity:(1)real-time monitors and data collections;(2)identifications,diagnoses and predictions of manufacturing complexity;(3)fortification of human-machine interaction.A case study of airplane manufacturing is presented to illustrate the proposed framework.

TAD-Net:An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor[version 1;peer review:2 approved]

Background:Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly.Human action has a significant impact on the production safety and efficiency of a shop-floor,however,because of the high individual initiative of humans,it is difficult to realize real-time action detection in a digital twin shop-floor.Methods:We proposed a real-time detection approach for shop-floor production action.This approach used the sequence data of continuous human skeleton joints sequences as the input.We then reconstructed the Joint Classification-Regression Recurrent Neural Networks(JCR-RNN)based on Temporal Convolution Network(TCN)and Graph Convolution Network(GCN).We called this approach the Temporal Action Detection Net(TAD-Net),which realized real-time shop-floor production action detection.Results:The results of the verification experiment showed that our approach has achieved a high temporal positioning score,recognition speed,and accuracy when applied to the existing Online Action Detection(OAD)dataset and the Nanjing University of Science and Technology 3 Dimensions(NJUST3D)dataset.TAD-Net can meet the actual needs of the digital twin shop-floor.Conclusions:Our method has higher recognition accuracy,temporal positioning accuracy,and faster running speed than other mainstream network models,it can better meet actual application requirements,and has important research value and practical significance for standardizing shop-floor production processes,reducing production security risks,and contributing to the understanding of real-time production action.

水利大模型的建设思路、构建框架与应用场景初探

发展新质生产力是推动高质量发展的内在要求和重要着力点。推进数字孪生水利建设是推动新阶段水利高质量发展的显著标志和重要路径,模型和知识是数字孪生水利建设的关键所在。因此,按照“需求牵引、应用至上、数字赋能、提升能力”要求,针对水利业务需求融合大模型、水利专业模型和知识而形成“水利大模型”,是发展水利新质生产力的重要引擎。阐述了水利大模型的概念和构建的重要意义,介绍了构建的思路与总体框架,分析了需突破的关键技术,介绍了行业应用场景。研究成果可为人工智能大模型在水利行业落地提供引导和借鉴。

基于数字孪生的模块化生产系统实践教学平台设计与实现

该文将数字孪生技术应用于模块化生产系统(modular production system,MPS)的智能化改造中,构建了基于数字孪生的MPS实践教学平台。依托监控系统和虚实交互等关键技术,增加了MPS数字孪生体的虚拟建模、虚实联调和PLC程序软件在环/硬件在环调试等实训环节;验证了MPS实践教学平台设计方案的可行性;制定了新的教学框架及详细的实训内容和考核方案。最后,将实训内容与大学生竞赛相结合,提高了学生对智能制造、工业生产的认知能力和实践动手能力。

数字孪生水利水电工程扩展数据结构建立方法

针对水利水电工程专业众多,建模工具的差异会导致专业协同或阶段性成果移交时出现数据移交难度大甚至数据丢失的问题,创新性地提出通过按需扩展IFC(工业基础类)原数据结构,建立数字孪生水利水电工程扩展数据结构方法,形成面向语义表达、几何表达和属性集表达的水利水电工程IFC扩展表达方式,为水利水电工程易丢失的数据提供标准化存储空间。以此为基础,基于Autodesk Revit软件并借助Revit IFC Exporter开源工具包,研发包含几何信息、属性信息等的扩展IFC导出功能,对坝体结构进行扩展和导出,验证扩展数据结构建立方法的可操作性。最终通过数据移交案例,证明了该方法能够有效保证工程信息在移交过程中不丢失。

融合Unity3D的缆索起重机安全运行数字孪生模型构建方法

为降低缆索起重机(下文简称为缆机)运行的安全风险,分析缆机运行流程,提取缆机安全运行知识语义,通过物联网技术获取实时数据,利用3DS Max软件构建大坝缆机运行初始场景,实现孪生模型三维可视化,将模型导入Unity3D引擎,使用高清渲染管线对模型进行渲染,改善视觉效果,编写C#脚本对缆机运行全过程进行安全模拟仿真。研究结果表明:应用本文所构建的缆机安全运行数字孪生模型监测隐患与故障,综合准确率达到96.7%,可有效实现缆机运行过程的安全监控、参数化控制以及可视化展示。研究结果可为缆机施工过程的安全控制提供技术支持。

装配式建筑PC构件产销一体化管理系统研究

混凝土预制(Precast Concrete,PC)构件是装配式建筑主体结构的基础,提升其生产的智能化、信息化水平尤为关键。文章以PC构件厂的生产管理模式和普遍管理现状为研究对象,基于PC构件生产线模式的需求分析,设计管理系统的基本组成架构、静态和动态信息模块,完成管理系统的开发。通过订单跟踪和生产管理在管理系统中的集成,完成PC构件的多品种、小批量生产的一体化管理;并基于数字孪生模型与PC构件生产设备之间的信息交互,实现订单跟踪的可视化管理。结果表明:管理系统通过对PC构件的订单跟踪和质量管理,可实现订单数据的全面分析和精细化管理,并有效提升生产效率和决策管理水平。

数字孪生水利赋能水利新质生产力发展框架研究

发展新质生产力是推动高质量发展的内在要求和重要着力点。进入新发展阶段,数字孪生水利因其具有高科技、高效能、高质量特征,成为水利新质生产力的最显著标志和最有力抓手。水利部党组将推进数字孪生水利建设作为推动新阶段水利高质量发展的显著标志和重要路径,其本质就是在发展水利新质生产力。基于新质生产力的科学内涵和重大意义,探讨辨析建设数字孪生水利与发展新质生产力之间的内在联系和逻辑,并从建设行动、关键技术、标准体系、人才培养等方面提出以数字孪生水利驱动引领水利新质生产力发展的实施路径。