Research

24 pages, 7212 KiB

Open AccessArticle

Continuous Dynamic Analysis Method and Case Verification of Cable Structure Based on Digital Twin

by Zeqiang Wang, Mingming Li, Zhansheng Liu, Majid Dezhkam, Yifeng Zhao and Yang Hu

Sustainability 2023, 15(22), 16125; https://doi.org/10.3390/su152216125 - 20 Nov 2023

Viewed by 703

The safety and quality of cable structure construction necessitate a comprehensive analysis approach. However, conventional methods suffer from difficulties in the temporal and spatial integration of construction information and low efficiency in construction analysis. This study proposes a multi-dimensional digital twin model for [...] Read more.

The safety and quality of cable structure construction necessitate a comprehensive analysis approach. However, conventional methods suffer from difficulties in the temporal and spatial integration of construction information and low efficiency in construction analysis. This study proposes a multi-dimensional digital twin model for cable structure construction to optimize conventional calculation methods. Firstly, this study proposes a continuous dynamic analysis method for cable structures based on the digital twin, which reveals the mechanism behind the continuous dynamic analysis of cable structures. Furthermore, a multidimensional digital twin model is established, and the model is continuously corrected using real-time data collected by sensors. The intrinsic constitution equation and equilibrium equation are also corrected to improve the finite element analysis method of the cable structure. An intelligent simulation system for cable structures was developed and effectively applied to actual cable structure construction scenarios. The same finite element analysis model was used to calculate all stages from lifting to tension forming. Construction information fusion ensured continuous dynamic analysis with an average calculation accuracy higher than 97%. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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15 pages, 6019 KiB

Open AccessArticle

An Intelligent Evaluation Method for Service Safety of Cable Net Structures under Multiple Factors

by Zhansheng Liu, Zehua Zhang and Chao Yuan

Sustainability 2023, 15(21), 15633; https://doi.org/10.3390/su152115633 - 05 Nov 2023

Viewed by 874

Abstract

Various uncertainties often influence the serviceability of cable net structures, which can impact their structural safety performance. The accurate identification of sensitive factors during the structure’s service life and the determination of its serviceability state is crucial for achieving intelligent serviceability safety. In [...] Read more.

Various uncertainties often influence the serviceability of cable net structures, which can impact their structural safety performance. The accurate identification of sensitive factors during the structure’s service life and the determination of its serviceability state is crucial for achieving intelligent serviceability safety. In this paper, based on the digital twin model, a multi-factor-based assessment method for the serviceability safety of cable net structures was proposed. Firstly, the assessment method for serviceability safety under multi-factor influence was described in detail, outlining the specific workflow. Secondly, key indicators that affect the structural safety state are selected, and their range of variation is determined. Subsequently, a comprehensive dataset of sample data, considering long-term multi-factor influence, was constructed, through combined simulation using MATLAB 2021 and ANSYS 15.0. Finally, a support-vector-regression-based structural safety assessment model was established and validated. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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24 pages, 6218 KiB

Open AccessArticle

Methods and Applications of Safety Control for Cable Net Structure Considering Spatiotemporal Changes

by Zeqiang Wang, Zehua Zhang, Zhansheng Liu, Majid Dezhkam and Yifeng Zhao

Sustainability 2023, 15(18), 13922; https://doi.org/10.3390/su151813922 - 19 Sep 2023

Viewed by 699

Abstract

The construction of cable net structures is intricate, and the construction process itself is laborious. Conventional safety control measures during the construction of cable net structures involve monitoring cable forces and deformations at specific moments during the construction steps. However, these measures do [...] Read more.

The construction of cable net structures is intricate, and the construction process itself is laborious. Conventional safety control measures during the construction of cable net structures involve monitoring cable forces and deformations at specific moments during the construction steps. However, these measures do not guarantee adequate safety assurance. This paper proposes a method for the safety control of cable net structures, considering spatiotemporal changes, based on the concept of digital twins. This method enables real-time monitoring and control of the cable net construction process onsite, facilitating a comparative analysis between the mechanical and geometric information of the construction site and the real-time finite element simulation results. Such an approach ensures safety control throughout the construction process. Firstly, a twin model framework for safety control is established. Then, the methods for spatiotemporal representation, data collection, and processing at the construction site are analyzed. Finally, the proposed method is validated through its application to the Xiaotian Cultural and Sports Park project. The results demonstrate that this method can achieve real-time monitoring and control of cable net structure construction. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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16 pages, 4268 KiB

Open AccessArticle

Enhancing Zero-Carbon Building Operation and Maintenance: A Correlation-Based Data Mining Approach for Database Analysis

by Yuhong Zhao, Ruirui Liu, Zhansheng Liu, Yun Lu, Liang Liu, Jingjing Wang and Wenxiang Liu

Sustainability 2023, 15(18), 13671; https://doi.org/10.3390/su151813671 - 13 Sep 2023

Viewed by 675

Abstract

In the context of global climate change and the increasing focus on carbon emissions, carbon emission research has become a prominent area of study. However, research in this field inevitably involves extensive monitoring, and when the data become complex and chaotic, the accuracy [...] Read more.

In the context of global climate change and the increasing focus on carbon emissions, carbon emission research has become a prominent area of study. However, research in this field inevitably involves extensive monitoring, and when the data become complex and chaotic, the accuracy of these data can be challenging to control, making it difficult to determine their reliability. This article starts by exploring the operational and maintenance data of zero-carbon buildings, aiming to uncover the correlation between energy consumption data and environmental data. This correlation is categorized into two main types: linear correlation and trend correlation. By establishing error degree calculations based on these correlation relationships, anomaly detection can be performed on the data. Analyzing the interrelationships between these datasets allows for the formulation of appropriate fitting equations, primarily consisting of linear and polynomial fits, all of which exhibit a determination coefficient exceeding 0.99. These fitting equations are then utilized to correct errors in the anomalous data, and the reasonableness of the fitting methods is demonstrated by examining the residual distribution. The final results align with the corresponding expectations, providing a concise and effective correction method for monitoring data in zero-carbon smart buildings. Importantly, this method exhibits a certain level of generality and can be applied to various scenarios within the realm of zero-carbon buildings. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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17 pages, 6081 KiB

Open AccessArticle

Compliance Checking on Topological Spatial Relationships of Building Elements Based on Building Information Models and Ontology

by Xuefeng Zhao, Lingli Huang, Zhe Sun, Xiongtao Fan and Meng Zhang

Sustainability 2023, 15(14), 10901; https://doi.org/10.3390/su151410901 - 12 Jul 2023

Viewed by 1096

Abstract

Compliance checking on the topological spatial relationships of building elements is vital for ensuring the safety and the quality of buildings. However, the complex topological spatial relationships of buildings are not usually expressed in the design scheme directly. Manual checking is still needed [...] Read more.

Compliance checking on the topological spatial relationships of building elements is vital for ensuring the safety and the quality of buildings. However, the complex topological spatial relationships of buildings are not usually expressed in the design scheme directly. Manual checking is still needed to analyze the design scheme and extract the spatial relationships. Such manual checking is always time consuming and prone to error. Therefore, this study has proposed a compliance checking method based on a building information model (BIM) and building ontologies for the automatic checking of topological spatial relationships. Firstly, the topological spatial relationships are well captured and represented according to the location relation of building elements. The checking rules are further established based on regulations. Then, the design information is extracted from the design model, mainly including the location information of building elements. Next, the review ontology is developed, and the design information is organized based on the ontology. Finally, the checking is completed based on the ontology and checking rules. The authors have validated the proposed method through a case study. The results show that the proposed method could help to achieve automatic compliance checking on topological spatial relationships of building elements. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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28 pages, 4913 KiB

Open AccessArticle

Modeling of Predictive Maintenance Systems for Laser-Welders in Continuous Galvanizing Lines Based on Machine Learning with Welder Control Data

by Jin-Seong Choi, So-Won Choi and Eul-Bum Lee

Sustainability 2023, 15(9), 7676; https://doi.org/10.3390/su15097676 - 07 May 2023

Viewed by 2142

Abstract

This study aimed to develop a predictive maintenance model using machine learning (ML) techniques to automatically detect equipment failures before line shutdowns due to equipment malfunctions, explicitly focusing on laser welders in the continuous galvanizing lines (CGLs) of a steel plant in Korea. [...] Read more.

This study aimed to develop a predictive maintenance model using machine learning (ML) techniques to automatically detect equipment failures before line shutdowns due to equipment malfunctions, explicitly focusing on laser welders in the continuous galvanizing lines (CGLs) of a steel plant in Korea. The study selected an auto-encoder (AE) as a base model, which has the strength of applying normal data and a long short-term memory (LSTM) model for application to time series data, such as equipment operation data. Here, a laser welder predictive maintenance model (LW-PMM) based on the LSTM-AE algorithm was developed by combining the technical advantages of both algorithms. Approximately 1500 types of data were collected, and approximately 200 were selected through preprocessing. The training and testing datasets were split at a ratio of 8:2, and the model parameters were optimized using 10-fold cross-validation. The performance evaluation of the LW-PMM resulted in an accuracy rate of 97.3%, a precision rate of 79.8%, a recall rate of 100%, and an F1-score of 88.8%. The precision of 79.8% compared to the 100% recall value indicated that although the model predicted all failures in the equipment as failures, 20.2% of them were duplicate values, which can be interpreted as one of the five failure signals being not an actual failure. As a result of the application to an actual CGL operation site, equipment abnormalities were detected for the first time 27 h before failure, resulting in a reduction of 18 h compared with the existing process. This study is unique because it started as a proof of concept (POC) and was validated in a production setting as a pilot system for the predictive maintenance of laser welders. We expect this study to be expanded and applied to steel production processes, contributing to digital transformation and innovation in the steel industry. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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20 pages, 1849 KiB

Open AccessArticle

The Digital Twin Modeling Method of the National Sliding Center for Intelligent Security

by Zhansheng Liu, Xiaotao Sun, Zhe Sun, Liang Liu and Xiaolin Meng

Sustainability 2023, 15(9), 7409; https://doi.org/10.3390/su15097409 - 29 Apr 2023

Cited by 1 | Viewed by 1242

Abstract

There are some problems in the security management of large stadiums, such as complex situations and a lack of coordination among systems. An intelligent security system can effectively improve the efficiency of security management. The digital twin concept is applied to intelligent security [...] Read more.

There are some problems in the security management of large stadiums, such as complex situations and a lack of coordination among systems. An intelligent security system can effectively improve the efficiency of security management. The digital twin concept is applied to intelligent security systems in large stadiums, and an intelligent security modeling method for large stadiums based on digital twin is proposed. The modeling method of the physical model is presented for the security equipment and building entities. The virtual model is based on geometric, physical, behavioral, and rule models. Considering the particularity of building security, the environmental model is added to describe the environmental information. The application mode of the digital twin model is proposed. In the security management process, multi-source data and virtual models are integrated to analyze and control the security management process of buildings, forming a closed loop of “perception-analysis-control” in security management. Taking the National Sliding Center as an example, this paper verifies the digital twin model and its operation mode of intelligent building security through several possible situations in the operation process of the stadium. The analysis of security data and evacuation path guidance in emergencies are simulated. The digital twin model for intelligent security integrated the building security data and simulation models to assist in identifying the types of dangers and the treatment of emergencies. Furthermore, the control of building equipment was integrated into the security system. The digital twin model for intelligent security improved the integration and intelligence of the security system. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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21 pages, 69004 KiB

Open AccessArticle

Intelligent Damage Assessment for Post-Earthquake Buildings Using Computer Vision and Augmented Reality

by Zhansheng Liu, Jie Xue, Naiqiang Wang, Wenyan Bai and Yanchi Mo

Sustainability 2023, 15(6), 5591; https://doi.org/10.3390/su15065591 - 22 Mar 2023

Cited by 3 | Viewed by 1766

Abstract

The most negative effects caused by earthquakes are the damage and collapse of buildings. Seismic building retrofitting and repair can effectively reduce the negative impact on post-earthquake buildings. The priority to repair the construction after being damaged by an earthquake is to perform [...] Read more.

The most negative effects caused by earthquakes are the damage and collapse of buildings. Seismic building retrofitting and repair can effectively reduce the negative impact on post-earthquake buildings. The priority to repair the construction after being damaged by an earthquake is to perform an assessment of seismic buildings. The traditional damage assessment method is mainly based on visual inspection, which is highly subjective and has low efficiency. To improve the intelligence of damage assessments for post-earthquake buildings, this paper proposed an assessment method using CV (Computer Vision) and AR (Augmented Reality). Firstly, this paper proposed a fusion mechanism for the CV and AR of the assessment method. Secondly, the CNN (Convolutional Neural Network) algorithm and gray value theory are used to determine the damage information of post-earthquake buildings. Then, the damage assessment can be visually displayed according to the damage information. Finally, this paper used a damage assessment case of seismic-reinforced concrete frame beams to verify the feasibility and effectiveness of the proposed assessment method. Full article

(This article belongs to the Special Issue Industry 4.0 Digital Transformation for Intelligent Construction, Operation and Maintenance)

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