Advances in Intelligent Construction, Operation and Maintenance

Topic Information

Dear Colleagues,

Intelligent construction, operation, and maintenance combines modern information technology, the life-cycle concept, and traditional engineering mode, which is the research frontier in civil engineering. Specifically, intelligent construction, operation, and maintenance utilizes information technology, such as digitalization, network, intelligence, data, calculation power, and advanced algorithm, to realize the collaboration of project planning, design, implementation, operation, and maintenance through digital construction resources, standardized model, information interaction, visual recognition, high-performance computing, and intelligent decision. Intelligent construction, operation, and maintenance can maximize the value of the project, minimize the cost of the project, improve the industrial structure, enhance structural safety, reduce the investment in maintenance, and deliver green and sustainable intelligent engineering products and services to users. The full achievements of the above objectives and benefits need well-coordinated interdisciplinary research for the full adaptation of innovative technologies developed in other disciplines to applications in civil engineering. Therefore, intelligent construction, operation, and maintenance involve three major fields: building information model technology, internet of things technology, and artificial intelligence technology, including dozens of research topics such as information monitoring, data mining, structural analysis, automated construction, remote control, optimal operation, safety assessment, and renewable materials. In the past several years, researchers throughout the world have carried out intensive research in this community and made remarkable progress. The purpose of this Special Issue is to attract the latest progress in intelligent construction, operation, and maintenance and to integrate scholars in various fields to discuss the current challenges in this community. The topics of interest for publication include but are not limited to:

• Building information model technology
• Intelligent building technology
• Structural health monitoring technology
• Intelligent sensing technology
• Artificial intelligence technology
• Computer vision technology
• Structural retrofit technology
• Green and recyclable materials

Prof. Dr. Guangdong Zhou
Dr. Songhan Zhang
Dr. Jian Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Buildings buildings	3.8	3.1	2011	14.6 Days	CHF 2600
Journal of Marine Science and Engineering jmse	2.9	3.7	2013	15.4 Days	CHF 2600
Materials materials	3.4	5.2	2008	13.9 Days	CHF 2600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400

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Published Papers (22 papers)

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29 pages, 5990 KiB  

Open AccessArticle

Multi-Objective Optimization for Ship Scheduling with Port Congestion and Environmental Considerations

by Xin Wen, Qiong Chen, Yu-Qi Yin, Yui-yip Lau and Maxim A. Dulebenets

J. Mar. Sci. Eng. 2024, 12(1), 114; https://doi.org/10.3390/jmse12010114 - 07 Jan 2024

Cited by 1 | Viewed by 1006

Abstract

Over the past several years, port congestion has become a severe problem, as ships are often not able to reach a series of ports based on the designed schedule, which induces changes in the schedules associated with port operations. Moreover, customers can not [...] Read more.

Over the past several years, port congestion has become a severe problem, as ships are often not able to reach a series of ports based on the designed schedule, which induces changes in the schedules associated with port operations. Moreover, customers can not receive their cargo in a timely manner because of port congestion. This is not only an internal problem within the shipping industry but also calls for collaboration between shipping lines and their upstream or downstream members in the maritime supply chain, including shippers and port operators. This study concentrates on the tactical planning problem for optimizing ship schedules to determine the number of ships, the projected maximum speed, and the ship service schedule, which is set for a company on a certain route. We develop a novel multi-objective programming model for the green vessel scheduling problem under port congestion, and queuing theory is used to calculate the uncertain queuing times at ports. The ultimate goal of developing this model is to maximize cost efficiency, service reliability, and environmental benefits. A multi-objective grey wolf optimizer algorithm is introduced for solving this problem, which shows some computational advantages compared to the NSGA-II algorithm commonly used at the most advanced level. Experimental results verify the application of the model and confirm that more congested periods induce more service unreliability issues rather than additional costs and emissions generated. To this end, the proposed methodology would allow designing better liner shipping schedules to alleviate port congestion and provide sustainable shipping services. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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19 pages, 9113 KiB  

Open AccessArticle

Feature-Centric Approach for Learning-Based Prediction of Pavement Marking Retroreflectivity from Mobile LiDAR Data

by Dmitry Manasreh, Munir D. Nazzal and Ala R. Abbas

Buildings 2024, 14(1), 62; https://doi.org/10.3390/buildings14010062 - 25 Dec 2023

Cited by 1 | Viewed by 648

Abstract

Given the crucial importance of pavement marking retroreflectivity in ensuring visibility for road safety, this research investigates the correlation between pavement marking reflectivity and LiDAR data. Empirical data were collected from eight road sections using both a handheld retroreflectometer and a mobile LiDAR. [...] Read more.

Given the crucial importance of pavement marking retroreflectivity in ensuring visibility for road safety, this research investigates the correlation between pavement marking reflectivity and LiDAR data. Empirical data were collected from eight road sections using both a handheld retroreflectometer and a mobile LiDAR. The approach proposed focuses on extracting important features from pavement marking regions of the LiDAR point cloud. A comprehensive feature extraction and feature selection process was employed. In addition, a well-rounded selection of learning algorithms was evaluated. A rigorous hold-out evaluation was incorporated, ensuring that the reported performance metrics were robustly generalizable. The best performing model was able to achieve an R² of 0.824 on unseen data. The findings of this study illuminate the potential for leveraging relatively inexpensive mobile LiDAR sensors in combination with machine learning techniques in conducting efficient pavement marking assessments, not only to detect completely degraded markings, but to accurately estimate retroreflective properties. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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28 pages, 9480 KiB  

Open AccessArticle

A Terrestrial Laser Scanning-Based Method for Indoor Geometric Quality Measurement

by Yi Tan, Xin Liu, Shuaishuai Jin, Qian Wang, Daochu Wang and Xiaofeng Xie

Remote Sens. 2024, 16(1), 59; https://doi.org/10.3390/rs16010059 - 22 Dec 2023

Viewed by 832

Abstract

The indoor geometric dimensions of a building are crucial for acceptance criteria. Traditional manual methods for measuring indoor geometric quality are labor-intensive, time-consuming, error-prone, and yield non-reproducible results. With the advancement of ground-based laser scanning technology, the efficient and precise measurement of geometric [...] Read more.

The indoor geometric dimensions of a building are crucial for acceptance criteria. Traditional manual methods for measuring indoor geometric quality are labor-intensive, time-consuming, error-prone, and yield non-reproducible results. With the advancement of ground-based laser scanning technology, the efficient and precise measurement of geometric dimensions has become achievable. An indoor geometric quality measurement method based on ground-based laser scanning is presented in this paper. Initially, a coordinate transformation algorithm based on selected points was developed for conducting coordinate conversion. Subsequently, the Cube Diagonal-based Denoising algorithm, developed for point cloud denoising, was employed. Following that, architectural components such as walls, ceilings, floors, and openings were identified and extracted based on their spatial relationships. The measurement and visualization of the geometric quality of walls’ flatness, verticality, and opening dimensions were automated using fitting and simulation methods. Lastly, tests and validation were conducted to assess the accuracy and applicability of the proposed method. The experimental results demonstrate that time and human resources can be significantly saved using this method. The accuracy of this method in assessing wall flatness, verticality, and opening dimensions is 77.8%, 88.9%, and 95.9%, respectively. These results indicate that indoor geometric quality can be detected more accurately and efficiently compared to traditional inspection methods using the proposed method. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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19 pages, 9646 KiB  

Open AccessArticle

Identification of Critical Links in Urban Road Network Based on GIS

by Jingwen Yuan, Hualan Wang and Yannan Fang

Sustainability 2023, 15(20), 14841; https://doi.org/10.3390/su152014841 - 13 Oct 2023

Viewed by 1108

Abstract

A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro [...] Read more.

A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro level, GIS is used to assess the distribution of public facilities along the roads. At the meso level, a city’s road density distribution is evaluated. At the macro level, a spatial barrier model and a transportation network model are constructed to assess the network accessibility. An inverse distance weighting method is employed to interpolate the accessibility. Furthermore, a network topology is established, and the entropy method is utilized to evaluate the sections comprehensively. The sections are ranked based on the evaluation results to identify the critical links in the urban road network. The road-network data and points of interest (POI) data from the Anning District in Lanzhou are selected for a case study, and the results indicate that the top five critical links have scores of 0.641, 0.571, 0.570, 0.519, and 0.508, respectively. Considering the three indicators enhances the accuracy of critical section identification, demonstrating the effectiveness of the proposed method. Visualizing each indicator using GIS 10.7 provides a new approach to identifying critical links in urban road networks and offers essential theoretical support for urban planning. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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19 pages, 24207 KiB  

Open AccessArticle

Anticipating the Collapse of Urban Infrastructure: A Methodology Based on Earth Observation and MT-InSAR

by Ignacio Rodríguez-Antuñano, Joaquín Martínez-Sánchez, Manuel Cabaleiro and Belén Riveiro

Remote Sens. 2023, 15(15), 3867; https://doi.org/10.3390/rs15153867 - 04 Aug 2023

Viewed by 1031

Abstract

Large-scale infrastructure monitoring and vulnerability assessment are crucial for the preservation and maintenance of built environments. To ensure the safety of urban infrastructure against natural and man-made disasters, constant monitoring is crucial. To do so, satellite Earth observation (EO) is being proposed, particularly [...] Read more.

Large-scale infrastructure monitoring and vulnerability assessment are crucial for the preservation and maintenance of built environments. To ensure the safety of urban infrastructure against natural and man-made disasters, constant monitoring is crucial. To do so, satellite Earth observation (EO) is being proposed, particularly radar-based imaging, because it allows large-scale constant monitoring since radar signals are not blocked by clouds and can be collected during both day and night. The proposed methodology for large-scale infrastructure monitoring and vulnerability assessment is based on MT-InSAR time series analysis. The homogeneity of the year-to-year displacement trend between each point and its surrounding points is evaluated to determine whether the area is a stable or vulnerable zone. To validate the methodology, four case studies of recently collapsed infrastructures are analyzed. The results indicate the potential of the proposed methodology for predicting and preventing structural collapses. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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23 pages, 8289 KiB  

Open AccessArticle

A Constrained Programming Model for the Optimization of Industrial-Scale Scheduling Problems in the Shipbuilding Industry

by Javier Pernas-Álvarez and Diego Crespo-Pereira

J. Mar. Sci. Eng. 2023, 11(8), 1517; https://doi.org/10.3390/jmse11081517 - 29 Jul 2023

Viewed by 1154

Abstract

This work presents an innovative constrained programming model for solving a flexible job-shop scheduling problem with assemblies and limited buffer capacity based on a real case from the shipbuilding industry. Unlike the existing literature, this problem incorporates the manufacturing and assembly of blocks [...] Read more.

This work presents an innovative constrained programming model for solving a flexible job-shop scheduling problem with assemblies and limited buffer capacity based on a real case from the shipbuilding industry. Unlike the existing literature, this problem incorporates the manufacturing and assembly of blocks from subblocks to the final ship erection, while considering the limited buffer capacity due to the size of blocks, which has been often overlooked. The objectives considered are the minimization of the makespan and tardiness based on ship erection due dates. To demonstrate the model’s effectiveness, it is initially validated using various scheduling problems from the literature. Then, the model is applied to progressively challenging instances of the shipbuilding problem presented in this work. Finally, the optimization results are validated and analyzed using a comprehensive simulation model. Overall, this work contributes to reducing the gap between academia and industry by providing evidence of the convenience of the application of constrained programming models combined with simulation models on industrial-size scheduling problems within reasonable computational time. Moreover, the paper emphasizes originality by addressing unexplored aspects of shipbuilding scheduling problems and highlights potential future research, providing a robust foundation for further advancements in the field. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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21 pages, 11216 KiB  

Open AccessArticle

Experimental Investigation on a Novel Temperature-Controlled Phase Change Aggregate Concrete: Thermo-Mechanical Properties and Hydration Heat Control

by Yejia Wang, Chengjin Wang, Aibo Luo, Minqi Dong, Qian Su, Chenling Zhou, Zongyu Zhang and Yanfei Pei

Materials 2023, 16(15), 5269; https://doi.org/10.3390/ma16155269 - 27 Jul 2023

Viewed by 712

Abstract

To reduce the structural deterioration of mass concrete structures from temperature cracks, and lower energy consumption caused by the traditional mass concrete hydration heat cooling process, this paper reports the preparation of concrete temperature-controlled phase change aggregate (PCA) by a vacuum compaction method [...] Read more.

To reduce the structural deterioration of mass concrete structures from temperature cracks, and lower energy consumption caused by the traditional mass concrete hydration heat cooling process, this paper reports the preparation of concrete temperature-controlled phase change aggregate (PCA) by a vacuum compaction method using light and high-strength black ceramite and No. 58 fully refined paraffin wax as phase change material (PCM), and the encapsulation technology of the aggregate by using superfine cement and epoxy resin. Further, through laboratory tests, the cylinder compressive strength, thermal stability and mixing breakage rate of the encapsulated PCA were tested, and the differences in mechanical properties such as compressive strength, flexural strength and splitting tensile strength between phase change aggregate concrete (PCAC) and ordinary concrete were studied. A test method was designed to test the heat storage effect of PCA, and the temperature control effect of PCAC was analyzed based on the law of conservation of energy. The research conclusions are as follows: (1) Both superfine cement and epoxy resin shells increase the strength of the aggregate, with the epoxy resin increasing it more than the superfine cement. The thermal stabilization of the PCA is good after encapsulation of superfine cement and epoxy resin. However, PCA encapsulated in superfine cement is more easily crushed than that encapsulated in epoxy resin. (2) Under the condition of water bath heating and semi-insulation, when the water bath temperature reaches 85 °C, the temperature difference between the PCA and the common stone aggregate can be up to 6 °C. Based on the law of energy conservation, the test results will be converted to mass concrete with the same volume of aggregate mixture;, the difference of PCAC and ordinary concrete temperature can be up to 10 °C, so the temperature control effect is significant. (3) The mechanical properties of PCAC with 100% aggregate replacement rate compared to ordinary concrete are reduced to varying degrees, and the performance decline of the epoxy-encapsulated PCA is smaller than that encapsulated with superfine cement; in an actual project, it is possible to improve the concrete grade to make up for this defect. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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20 pages, 3440 KiB  

Open AccessArticle

Hyperspectral Image Classification Based on Dense Pyramidal Convolution and Multi-Feature Fusion

by Junsan Zhang, Li Zhao, Hongzhao Jiang, Shigen Shen, Jian Wang, Peiying Zhang, Wei Zhang and Leiquan Wang

Remote Sens. 2023, 15(12), 2990; https://doi.org/10.3390/rs15122990 - 08 Jun 2023

Cited by 2 | Viewed by 1276

Abstract

In recent years, hyperspectral image classification techniques have attracted a lot of attention from many scholars because they can be used to model the development of different cities and provide a reference for urban planning and construction. However, due to the difficulty in [...] Read more.

In recent years, hyperspectral image classification techniques have attracted a lot of attention from many scholars because they can be used to model the development of different cities and provide a reference for urban planning and construction. However, due to the difficulty in obtaining hyperspectral images, only a limited number of pixels can be used as training samples. Therefore, how to adequately extract and utilize the spatial and spectral information of hyperspectral images with limited training samples has become a difficult problem. To address this issue, we propose a hyperspectral image classification method based on dense pyramidal convolution and multi-feature fusion (DPCMF). In this approach, two branches are designed to extract spatial and spectral features, respectively. In the spatial branch, dense pyramid convolutions and non-local blocks are used to extract multi-scale local and global spatial features in image samples, which are then fused to obtain spatial features. In the spectral branch, dense pyramidal convolution layers are used to extract spectral features in image samples. Finally, the spatial and spectral features are fused and fed into fully connected layers to obtain classification results. The experimental results show that the overall accuracy (OA) of the method proposed in this paper is 96.74%, 98.10%, 98.92% and 96.67% on the four hyperspectral datasets, respectively. Significant improvements are achieved compared to the five methods of SVM, SSRN, FDSSC, DBMA and DBDA for hyperspectral classification. Therefore, the proposed method can better extract and exploit the spatial and spectral information in image samples when the number of training samples is limited. Provide more realistic and intuitive terrain and environmental conditions for urban planning, design, construction and management. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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22 pages, 19078 KiB  

Open AccessArticle

A Location-Allocation Model with Obstacle and Capacity Constraints for the Layout Optimization of a Subsea Transmission Network with Line-Shaped Conduction Structures

by Cheng Hong, Yuxi Wang and Segen F. Estefen

J. Mar. Sci. Eng. 2023, 11(6), 1171; https://doi.org/10.3390/jmse11061171 - 02 Jun 2023

Cited by 1 | Viewed by 1265

Abstract

The idea of this paper comes from the need for a practical layout design for the subsea pipe line network and the power transmission network of offshore wind farms with subsea cables, which are both subsea transmission networks with line-shaped conduction structures. In [...] Read more.

The idea of this paper comes from the need for a practical layout design for the subsea pipe line network and the power transmission network of offshore wind farms with subsea cables, which are both subsea transmission networks with line-shaped conduction structures. In this paper, this practical need is treated as an location-allocation problem, with the objective of minimizing the total cost, and a mixed-integer linear programming model (MILP) for layout optimization is developed. Through the model, the locations of service centers and theit corresponding sizes, the allocations between customers and service centers, as well as the transmission routes can all be figured out. This work makes two key contributions. First, facilities’ capacity restrictions and the avoidance of subsea obstacles are both integrated, making the description of the layout closer to practical situations. Secondly, a “global to local” search process based on the Delaunay triangulation method is constructed to solve the model, resulting in a high-quality solution. An offshore field layout design scenario is taken as a case study, through which the validity, feasibility, and stability of the proposed model, as well as the solution strategy, are presented. Furthermore, in the case study, the effect of the manifold number on the layout optimization is analyzed, indicating the flexibility of the model’s applications. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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22 pages, 5353 KiB  

Open AccessArticle

An Information Management Model for Addressing Residents’ Complaints through Artificial Intelligence Techniques

by Jordana Bazzan, Márcia Elisa Echeveste, Carlos Torres Formoso, Bernardo Altenbernd and Márcia Helena Barbian

Buildings 2023, 13(3), 737; https://doi.org/10.3390/buildings13030737 - 11 Mar 2023

Cited by 7 | Viewed by 2086

Abstract

Construction companies usually record customer complaints as unstructured texts, resulting in unsuitable information to understand defect occurrences. Moreover, complaint databases are often manually classified, which is time-consuming and error-prone. However, previous studies have not provided guidance on how to improve customer complaint data [...] Read more.

Construction companies usually record customer complaints as unstructured texts, resulting in unsuitable information to understand defect occurrences. Moreover, complaint databases are often manually classified, which is time-consuming and error-prone. However, previous studies have not provided guidance on how to improve customer complaint data collection and analysis. This research aims to devise an information management model for customer complaints in residential projects. Using Design Science Research, a study was undertaken at a Brazilian residential building company. Multiple sources of evidence were used, including interviews, participant observations, and analysis of an existing database. Natural language processing (NLP) was used to build a word menu for customers to lodge a complaint. Moreover, a recommendation system was proposed based on machine learning (ML) and hierarchical defect classification. The system was designed to indicate which defects should be investigated during inspections. The main outcome of this investigation is an information management model that provides an effective classification system for customer complaints, supported by artificial intelligence (AI) applications that improve data collection, and introduce some degree of automation to warranty services. The main theoretical contribution of the study is the use of advanced data management approaches for managing complaints in residential building projects, resulting in the combination of inputs from technical and customer perspectives to support decision-making. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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19 pages, 10319 KiB  

Open AccessArticle

Rapid Compaction Monitoring and Quality Control of Embankment Dam Construction Based on UAV Photogrammetry Technology: A Case Study

by Han Yin, Chun Tan, Wen Zhang, Chen Cao, Xinchuan Xu, Jia Wang and Junqi Chen

Remote Sens. 2023, 15(4), 1083; https://doi.org/10.3390/rs15041083 - 16 Feb 2023

Cited by 3 | Viewed by 2144

Abstract

The compaction quality of embankment dams directly affects the safe operation of power stations. The traditional monitoring method has the shortcomings of limited sample and time consumption. Compaction quality can be reflected by the compression ratio (CR) of the filling material. [...] Read more.

The compaction quality of embankment dams directly affects the safe operation of power stations. The traditional monitoring method has the shortcomings of limited sample and time consumption. Compaction quality can be reflected by the compression ratio (CR) of the filling material. A novel method based on unmanned aerial vehicle (UAV) photogrammetry technology, which can rapidly acquire the CR of the entire filling area, is proposed in the present paper. Specifically, the CR nephogram is obtained by processing the terra information of the compaction body collected by the UAV. Validation of the CR results is performed by comparing them with the results obtained via leveling measurements. Mean absolute error between CR results and leveling measurements results is less than 1%, and the corresponding settlement value error is millimeter-level, reflecting a fairly good agreement. Furthermore, the reduced-scale experiment shows that the UAV-based CR method is more stable than manual measurements, and the efficiency is increased by more than five times, which meets the requirements of compaction quality monitoring and quality control. The CR nephogram obtained can reflect the compaction quality information rapidly, comprehensively, and accurately, thereby guiding the quality control of embankment dam construction. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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14 pages, 3823 KiB  

Open AccessArticle

Sustainable Analysis of Insulator Fault Detection Based on Fine-Grained Visual Optimization

by Linfeng Wang, Heng Wan, Deqing Huang, Jiayao Liu, Xuliang Tang and Linfeng Gan

Sustainability 2023, 15(4), 3456; https://doi.org/10.3390/su15043456 - 14 Feb 2023

Cited by 3 | Viewed by 1378

Abstract

Insulators of the kind used for overhead transmission lines institute important kinds of insulation control, namely, electrical insulation and mechanical fixing. Because of their large exposure to the environment, they are affected by factors such as climate, temperature, durability, the easy occurrence of [...] Read more.

Insulators of the kind used for overhead transmission lines institute important kinds of insulation control, namely, electrical insulation and mechanical fixing. Because of their large exposure to the environment, they are affected by factors such as climate, temperature, durability, the easy occurrence of explosions, damage, the threat of going missing, and other faults. These seriously influence the safety of the power transmission, so insulation monitoring must be conducted. With the development of unmanned technology, the staff used unmanned aircraft to take aerial photos of the detected insulators, and the insulator images were obtained by naked eye observation. Although this method looks very reliable, in practice, due to the large quantity of insulator-collected seismic data, and the complex background, workers are usually relying on their experience to make judgements, so it is easy for mistakes to appear. In recent years, with the rapid development of computer technology, more and more attention has been paid to fault detection and identification in insulators by computer-aided workers. In order to improve the detection accuracy of self-exploding insulators, especially in bad weather environments, and to overcome the influence of fog on target detection, a regression attention convolutional neural network is used for optimization. Through the recursive operation of multi-scale attention, multi-scale feature information is connected in series, the regional focus is recursively generated from coarse to fine, and the target region is detected to achieve optimal results. The experimental results show that the proposed method can effectively improve the fault diagnosis ability of insulators. Compared with the accuracy of other basic models, such as FCAN and MG-CNN, the accuracy of RA-CNN in multi-layer cascade optimization is higher than that in the previous two models, which is 74.9% and 75.6%, respectively. In addition, the results of the ablation experiments at different scales showed that the identification results of different two-level combinations were 78.2%, 81.4%, and 83.6%, and the accuracy of selecting three-level combinations was up to 85.3%, which was significantly higher than the other models. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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22 pages, 4977 KiB  

Open AccessArticle

Geometric Quality Assessment of Prefabricated Steel Box Girder Components Using 3D Laser Scanning and Building Information Model

by Yi Tan, Limei Chen, Qian Wang, Shenghan Li, Ting Deng and Dongdong Tang

Remote Sens. 2023, 15(3), 556; https://doi.org/10.3390/rs15030556 - 17 Jan 2023

Cited by 6 | Viewed by 1759

Abstract

Prefabricated steel box girders (SBGs) are widely adopted in bridge engineering due to their light weight and low lifecycle cost. To smoothly assemble SBG components on a construction site, it is necessary to inspect their geometric quality and ensure that all the as-is [...] Read more.

Prefabricated steel box girders (SBGs) are widely adopted in bridge engineering due to their light weight and low lifecycle cost. To smoothly assemble SBG components on a construction site, it is necessary to inspect their geometric quality and ensure that all the as-is SBG components have the correct dimensions. However, the traditional inspection method is time-consuming and error-prone. This study developed a non-contact geometric quality assessment technique based on 3D laser scanning to accurately assess the locations and dimensions of SBG components. First, a robust normal-based region-growing algorithm was developed to divide the SBG components into segments with different labels. The scanned data related to the T ribs were then extracted through the proposed subtraction algorithm after the identification of the steel cabin. Lastly, the required items for geometric quality inspection were calculated based on the extracted as-is SBG components. The feasibility of the proposed geometric quality assessment method was validated through a real SBG project. Field test results showed that the developed inspection technique could assess the geometric quality of prefabricated SBG components in a more accurate and efficient manner compared to traditional measurement approaches. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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17 pages, 7434 KiB  

Open AccessArticle

Inspection-Nerf: Rendering Multi-Type Local Images for Dam Surface Inspection Task Using Climbing Robot and Neural Radiance Field

by Kunlong Hong, Hongguang Wang and Bingbing Yuan

Buildings 2023, 13(1), 213; https://doi.org/10.3390/buildings13010213 - 12 Jan 2023

Cited by 2 | Viewed by 3180

Abstract

For the surface defects inspection task, operators need to check the defect in local detail images by specifying the location, which only the global 3D model reconstruction can’t satisfy. We explore how to address multi-type (original image, semantic image, and depth image) local [...] Read more.

For the surface defects inspection task, operators need to check the defect in local detail images by specifying the location, which only the global 3D model reconstruction can’t satisfy. We explore how to address multi-type (original image, semantic image, and depth image) local detail image synthesis and environment data storage by introducing the advanced neural radiance field (Nerf) method. We use a wall-climbing robot to collect surface RGB-D images, generate the 3D global model and its bounding box, and make the bounding box correspond to the Nerf implicit bound. After this, we proposed the Inspection-Nerf model to make Nerf more suitable for our near view and big surface scene. Our model use hash to encode 3D position and two separate branches to render semantic and color images. And combine the two branches’ sigma values as density to render depth images. Experiments show that our model can render high-quality multi-type images at testing viewpoints. The average peak signal-to-noise ratio (PSNR) equals 33.99, and the average depth error in a limited range (2.5 m) equals 0.027 m. Only labeled $2\%$ images of 2568 collected images, our model can generate semantic masks for all images with 0.957 average recall. It can also compensate for the difficulty of manual labeling through multi-frame fusion. Our model size is 388 MB and can synthesize original and depth images of trajectory viewpoints within about 200 m² dam surface range and extra defect semantic masks. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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20 pages, 1751 KiB  

Open AccessArticle

Analysis of the Drivers of Highway Construction Companies Adopting Smart Construction Technology

by Zhichao Zhou, Yikun Su, Zhizhe Zheng and Yilin Wang

Sustainability 2023, 15(1), 703; https://doi.org/10.3390/su15010703 - 30 Dec 2022

Cited by 3 | Viewed by 1710

Abstract

In this study, we aimed to identify the influencing factors that drive highway construction companies to adopt smart construction technologies. Using expert interviews and expert scoring, we collected interview data from 25 experts in the field and we proposed the TOSE framework based [...] Read more.

In this study, we aimed to identify the influencing factors that drive highway construction companies to adopt smart construction technologies. Using expert interviews and expert scoring, we collected interview data from 25 experts in the field and we proposed the TOSE framework based on the TOE framework, identifying four dimensions and fourteen influencing factors. We analyzed the results using the Fuzzy DEMATEL-ISM method, and we then summarized the findings according to the evaluation criteria to determine the validity of the fourteen hypotheses and the extent to which they drive highway construction companies to adopt smart construction technologies. The findings of this paper are of high value to decision makers and participants in highway construction companies, as well as to other companies in the construction industry, in their decision to adopt smart construction technologies. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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20 pages, 5695 KiB  

Open AccessArticle

Semi-Supervised Random Forest Methodology for Fault Diagnosis in Air-Handling Units

by Guofeng Ma and Haoran Ding

Buildings 2023, 13(1), 14; https://doi.org/10.3390/buildings13010014 - 21 Dec 2022

Cited by 1 | Viewed by 1179

Abstract

Air-handling units have been widely used in indoor air conditioning and circulation in modern buildings. The data-driven FDD method has been widely used in the field of industrial roads, and has been widely welcomed because of its extensiveness and flexibility in practical applications. [...] Read more.

Air-handling units have been widely used in indoor air conditioning and circulation in modern buildings. The data-driven FDD method has been widely used in the field of industrial roads, and has been widely welcomed because of its extensiveness and flexibility in practical applications. Under the condition of sufficient labeled data, previous studies have verified the utility and value of various supervised learning algorithms in FDD tasks. However, in practice, obtaining sufficient labeled data can be very challenging, expensive, and will consume a lot of time and manpower, making it difficult or even impractical to fully explore the potential of supervised learning algorithms. To solve this problem, this study proposes a semi-supervised FDD method based on random forest. This method adopts a self-training strategy for semi-supervised learning and has been verified in two practical applications: fault diagnosis and fault detection. Through a large number of data experiments, the influence of key learning parameters is statistically represented, including the availability of marked data, the number of iterations of maximum half-supervised learning, and the threshold of utilization of pseudo-label data. The results show that the proposed method can effectively utilize a large number of unlabeled data, improve the generalization performance of the model, and improve the diagnostic accuracy of different column categories by about 10%. The results are helpful for the development of advanced data-driven fault detection and diagnosis tools for intelligent building systems. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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23 pages, 4876 KiB  

Open AccessArticle

Probabilistic Life Prediction of Tunnel Boring Machine under Wearing Conditions with Incomplete Information

by Xianlei Fu, Maozhi Wu and Limao Zhang

Buildings 2022, 12(11), 1959; https://doi.org/10.3390/buildings12111959 - 11 Nov 2022

Cited by 3 | Viewed by 1760

Abstract

This paper developed a data analysis approach to estimate the probabilistic life of an earth pressure balance (EPB) tunnel boring machine (TBM) under wearing conditions with incomplete information. The marginal reliability function of each system component of TBM is derived based on data [...] Read more.

This paper developed a data analysis approach to estimate the probabilistic life of an earth pressure balance (EPB) tunnel boring machine (TBM) under wearing conditions with incomplete information. The marginal reliability function of each system component of TBM is derived based on data collected from the site. The structure of the failure framework was determined based on the evaluation of influencing factors, including the wearing of the cutter head panel and screw conveyor. The joint distribution model was built by utilizing the best-fit copula function and the remaining reliable mining distance can be predicted from this model. Real data of the remaining thickness of the wearing resistance structure of the cutter head panel and screw conveyor from an earth pressure balance (EPB) TBM were captured. A realistic metro tunneling project in China was utilized to examine the applicability and effectiveness of the developed approach. The results indicate that: (1) With the selection of normal distribution and Gumbel copula as the best-fit marginal distribution function and copula function, the reliable mining distance was predicted as 4.0834 km when the reliability equaled 0.2. (2) The copula function was necessary to be considered to assess the joint distribution of the reliability function, as the predicted mining distance reduces significantly to 3.9970 km if assumed independent. (3) It enables the user to identify the weak component in the machinery and significantly improve the reliable mining distance to 4.5075 km by increasing the initial thickness of the screw conveyor by 0.5 mm. This approach can be implemented to minimize the risk of unintended TBM breakdown and improve the tunneling efficiency by reducing unnecessary cutter head intervention during the mining process. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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16 pages, 2986 KiB  

Open AccessArticle

Current Challenges in the Adoption of Digital Visual Management at Construction Sites: Exploratory Case Studies

by Ana Reinbold, Eelon Lappalainen, Olli Seppänen, Antti Peltokorpi and Vishal Singh

Sustainability 2022, 14(21), 14395; https://doi.org/10.3390/su142114395 - 03 Nov 2022

Cited by 1 | Viewed by 1678

Abstract

In the construction industry, digitalisation has led to increasing efforts to improve construction management using digital visual management (VM) devices. Although the amount of research on digital VM (DVM) in the design phase and in the management of construction sites has also increased, [...] Read more.

In the construction industry, digitalisation has led to increasing efforts to improve construction management using digital visual management (VM) devices. Although the amount of research on digital VM (DVM) in the design phase and in the management of construction sites has also increased, its implementation during the production phase and by construction crews remains limited. The objective of this study is to explore the adoption of DVM in construction sites, assess construction workers’ experiences regarding digital and analogue VM devices, and understand the challenges that hinder the adoption of such devices. This study used a mixed method approach, combining qualitative and quantitative research. Data included visual site explorations, surveys of construction workers and crew managers, and unstructured interviews with site managers and development directors to assess the use of DVM devices in construction sites, the need for them and their current implementation. The findings showed that VM should be conveniently located near the job site instead of the office site, which is the current situation. Construction crews who experienced more production and schedule disruptions reported that VM supported their work, compared with crews that had fewer disruptions. VM devices on construction sites are analogue, and their usage continues to be in construction site management, which perpetuates information silos during construction projects. The findings of this study provide insights into the development and deployment of DVM devices on construction sites. Construction workers’ need for visual information close at hand is of interest to both scholars and practitioners in future research and development. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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19 pages, 8492 KiB  

Open AccessArticle

Numerical Simulation Methodology for Prefabricated Shear Walls Considering Stochastic Defects in Grouting Materials

by Baijian Tang, Jiawei Wang, Huiyuan Shi, Zhiyuan Xia, Yongjie Zhang and Li Chen

Buildings 2022, 12(11), 1859; https://doi.org/10.3390/buildings12111859 - 02 Nov 2022

Cited by 1 | Viewed by 1147

Abstract

The most used connection form for reinforced steel bars is the grouting sleeve using cement-based grouting materials. Hence, the quality of the grouting sleeve connection determines whether the performance of a precast concrete structure is equivalent to that of a cast in situ [...] Read more.

The most used connection form for reinforced steel bars is the grouting sleeve using cement-based grouting materials. Hence, the quality of the grouting sleeve connection determines whether the performance of a precast concrete structure is equivalent to that of a cast in situ concrete structure. However, several existing reasons, namely, insufficient grouting cement or poor construction controls and even stochastic bubble holes, lead to inevitable grouting defects. The behavior of precast concrete structures is affected dramatically. Considering the cost and efficiency of the analysis of precast concrete structures, the finite element method is still the most used method, but the simulation technology of structures considering stochastic defects in grouting materials is not sufficient. Herein, a simulation method considering stochastic defects in precast concrete structures is proposed, and the application of the method to grouting sleeves and shear wall structures is performed to verify its accuracy and feasibility. The construction of stochastic defects in grouting material is first realized through the Python scripter. Secondly, the mechanical parameters are obtained from the refined finite element analysis of grouting sleeves with material defects. Finally, based on the obtained mechanical properties of grouting sleeves, the behaviors of precast shear walls under blast loading are analyzed. The simulations of grouting sleeves under uniaxial tensile loading and precast concrete shear walls under blast loading both indicate that the proposed numerical method is feasible in solving the structural issues with stochastic defects in grouting materials. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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13 pages, 6578 KiB  

Open AccessArticle

Rapid Detection of Tools of Railway Works in the Full Time Domain

by Zhaohui Zheng, Yuncheng Luo, Shaoyi Li, Zhaoyong Fan, Xi Li, Jianping Ju, Mingyu Lin and Zijian Wang

Sustainability 2022, 14(20), 13662; https://doi.org/10.3390/su142013662 - 21 Oct 2022

Viewed by 1226

Abstract

Construction tool detection is an important link in the operation and maintenance management of professional facilities in public works. Due to the large number and types of construction equipment and the complex and changeable construction environment, manual checking and inventory are still required. [...] Read more.

Construction tool detection is an important link in the operation and maintenance management of professional facilities in public works. Due to the large number and types of construction equipment and the complex and changeable construction environment, manual checking and inventory are still required. It is very challenging to count the variety of tools in a full-time environment automatically. To solve this problem, this paper aims to develop a full-time domain target detection system based on a deep learning network for difficult, complex railway environment image recognition. First, for the different time domain images, the image enhancement network with brightness channel decision is used to set different processing weights according to the images in different time domains to ensure the robustness of image enhancement in the entire time domain. Then, in view of the collected complex environment and the overlapping placement of the construction tools, a lightweight attention module is added on the basis of YOLOX, which makes the detection more purposeful, and the features cover more parts of the object to be recognized to improve the model. Overall detection performance. At the same time, the CIOU loss function is used to consider the distance fully, overlap rate, and penalty between the two detection frames, which is reflected in the final detection results, which can bring more stable target frame regression and further improve the recognition accuracy of the model. Experiments on the railway engineering dataset show that our RYOLO achieves a mAP of 77.26% for multiple tools and a count frame rate of 32.25FPS. Compared with YOLOX, mAP increased by 3.16%, especially the AP of woven bags with a high overlap rate increased from 0.15 to 0.57. Therefore, the target detection system proposed in this paper has better environmental adaptability and higher detection accuracy in complex railway environments, which is of great significance to the development of railway engineering intelligence. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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22 pages, 7604 KiB  

Open AccessArticle

Recognition and Depth Estimation of Ships Based on Binocular Stereo Vision

by Yuanzhou Zheng, Peng Liu, Long Qian, Shiquan Qin, Xinyu Liu, Yong Ma and Ganjun Cheng

J. Mar. Sci. Eng. 2022, 10(8), 1153; https://doi.org/10.3390/jmse10081153 - 20 Aug 2022

Cited by 48 | Viewed by 2918

Abstract

To improve the navigation safety of inland river ships and enrich the methods of environmental perception, this paper studies the recognition and depth estimation of inland river ships based on binocular stereo vision (BSV). In the stage of ship recognition, considering the computational [...] Read more.

To improve the navigation safety of inland river ships and enrich the methods of environmental perception, this paper studies the recognition and depth estimation of inland river ships based on binocular stereo vision (BSV). In the stage of ship recognition, considering the computational pressure brought by the huge network parameters of the classic YOLOv4 model, the MobileNetV1 network was proposed as the feature extraction module of the YOLOv4 model. The results indicate that the mAP value of the MobileNetV1-YOLOv4 model reaches 89.25%, the weight size of the backbone network was only 47.6 M, which greatly reduced the amount of computation while ensuring the recognition accuracy. In the stage of depth estimation, this paper proposes a feature point detection and matching algorithm based on the ORB algorithm at sub-pixel level, that is, firstly, the FSRCNN algorithm was used to perform super-resolution reconstruction of the original image, to further increase the density of image feature points and detection accuracy, which was more conducive to the calculation of the image parallax value. The ships’ depth estimation results indicate that when the distance to the target is about 300 m, the depth estimation error is less than 3%, which meets the depth estimation needs of inland ships. The ship target recognition and depth estimation technology based on BSV proposed in this paper makes up for the shortcomings of the existing environmental perception methods, improves the navigation safety of ships to a certain extent, and greatly promotes the development of intelligent ships in the future. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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23 pages, 8905 KiB  

Open AccessArticle

AlgaeMask: An Instance Segmentation Network for Floating Algae Detection

by Xiaoliang Wang, Lei Wang, Liangyu Chen, Feng Zhang, Kuo Chen, Zhiwei Zhang, Yibo Zou and Linlin Zhao

J. Mar. Sci. Eng. 2022, 10(8), 1099; https://doi.org/10.3390/jmse10081099 - 11 Aug 2022

Cited by 4 | Viewed by 1588

Abstract

Video surveillance on the offshore booster station and around the coast is a effective way to monitor floating macroalgae. Previous studies on floating algae detection are mainly based on traditional image segmentation methods. However, these algorithms cannot effectively solve the problem of extracting [...] Read more.

Video surveillance on the offshore booster station and around the coast is a effective way to monitor floating macroalgae. Previous studies on floating algae detection are mainly based on traditional image segmentation methods. However, these algorithms cannot effectively solve the problem of extracting Ulva prolifra and Sargassum at different sizes and views. Recently, instance segmentation methods have achieved great success in computer vision applications. In this paper, based on the CenterMask network, a novel instance segmentation architecture named AlgaeMask is proposed for floating algae detection from the surveillance videos. To address the feature extraction ability of the network in the inter-dependencies for position and channel, we introduce a new OSA-V3 module with the dual-attention block, which consists of a position attention mechanism and channel attention mechanism. Meanwhile, scale-equalizing pyramid convolution is introduced to solve the problem of scale difference. Finally, we introduce the feature decoder module based on FCOS head and segmentation head to obtain the segmentation area of floating algae in each bounding box. The extensive experiment results show that the average precision of our AlgaeMask in the tasks of mask segmentation and box detection can reach 44.22% and 48.13%, respectively, which has 15.09% and 8.24% improvement over CenterMask. In addition, the AlgaeMask can meet the real-time requirements of floating algae detection. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance)

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