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A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 17991

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Special Issue Editors

Dr. Zheng-Yun Zhuang

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Guest Editor

Department of Civil Engineering, College of Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Interests: OR (methods/applications); computer science (CS); (big) data analytics; goal programming; DDDM (data-driven decision making); supply chain decisions; transportation; building information

Dr. Ying-Wu Yang

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Guest Editor

College of Landscape and Architecture, Zhejiang A&F University, Hangzhou 310023, China
Interests: structure health monitoring; structure vibration analysis; structural detection and strengthening

Prof. Dr. Ming-Hung Hsu

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Guest Editor

Department of Electrical Engineering, National Penghu University of Science and Technology, Magong 880011, Taiwan
Interests: vibration analysis; power systems; electromechanical integration; structural health diagnosis and systems; smart grids; wind power; photovoltaics
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Special Issue Information

Dear Colleagues,

This Special Issue (SI), entitled “Application of (Big) Data Analysis in A/E/C”, in Buildings has issued a call for papers. The Special Issue will focus on quality papers outlining novel contributions of the application of big data theory, modelling techniques, and methodologies as a tool for leveraging data analytics in the building industry. Contributions are also welcomed from related topics covering various application domains of the global A/E/C (architecture, engineering, and construction) industry, including, but not limited to, construction management (including building information, land use, etc.), structure engineering and health, materials, transportation management and facilities, and geotechnical engineering. Research involving or utilising theory and modelling methodologies during one, some, or all phases of data collection, data preprocessing, data analysis, prediction, and the decision support functions of the (big) data analysis process are welcomed. In addition to the papers with valuable insights and expressing novel ideas, data-relevant studies that demonstrate advancements in current methodologies or methodological processes for a specific application domain will also be considered.

Dr. Zheng-Yun Zhuang
Dr. Ying-Wu Yang
Prof. Dr. Ming-Hung Hsu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

construction
engineering
architecture
(big) data analytics
structural health/monitoring
geotechnical/materials
power/transportation systems
data curation and (pre-)processing
data analysis
prediction/forecasting
data-driven decision making

Published Papers (10 papers)

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Editorial

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6 pages, 197 KiB

Open AccessEditorial

Applications of (Big) Data Analysis in A/E/C

by Ming-Hung Hsu, Ying-Wu Yang and Zheng-Yun Zhuang

Buildings 2023, 13(6), 1442; https://doi.org/10.3390/buildings13061442 - 31 May 2023

Cited by 2 | Viewed by 720

Abstract

This editorial paper provides an overview of the Buildings Special Issue (SI), dedicated to the topic “Applications of (Big) Data Analysis in A/E/C” (where A/E/C stands for architecture, engineering, and construction) and the academic papers it includes [...] Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

Research

Jump to: Editorial

17 pages, 7165 KiB

Open AccessArticle

Applying Deep Learning and Single Shot Detection in Construction Site Image Recognition

by Li-Wei Lung and Yu-Ren Wang

Buildings 2023, 13(4), 1074; https://doi.org/10.3390/buildings13041074 - 19 Apr 2023

Cited by 4 | Viewed by 2435

Abstract

A construction site features an open field and complexity and relies mainly on manual labor for construction progress, quality, and field management to facilitate job site coordination and productive results. It has a tremendous impact on the effectiveness and efficiency of job site supervision. However, most job site workers take photos of the construction activities. These photos serve as aids for project management, including construction history records, quality, and schedule management. It often takes a great deal of time to process the many photos taken. Most of the time, the image data are processed passively and used only for reference, which could be better. For this, a construction activity image recognition system is proposed by incorporating image recognition through deep learning, using the powerful image extraction ability of a convolution neural network (CNN) for automatic extraction of contours, edge lines, and local features via filters, and feeding feature data to the network for training in a fully connected way. The system is effective in image recognition, which is in favor of telling minute differences. The parameters and structure of the neural network are adjusted for using a CNN. Objects like construction workers, machines, and materials are selected for a case study. A CNN is used to extract individual features for training, which improves recognizability and helps project managers make decisions regarding construction safety, job site configuration, progress control, and quality management, thus improving the efficiency of construction management. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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22 pages, 1230 KiB

Open AccessArticle

Determinants of Data Quality Dimensions for Assessing Highway Infrastructure Data Using Semiotic Framework

by Chenchu Murali Krishna, Kirti Ruikar and Kumar Neeraj Jha

Buildings 2023, 13(4), 944; https://doi.org/10.3390/buildings13040944 - 02 Apr 2023

Cited by 2 | Viewed by 1950

Abstract

The rapid accumulation of highway infrastructure data and their widespread reuse in decision-making poses data quality issues. To address the data quality issue, it is necessary to comprehend data quality, followed by approaches for enhancing data quality and decision-making based on data quality information. This research aimed to identify the critical data quality dimensions that affect the decision-making process of highway projects. Firstly, a state-of-the-art review of data quality frameworks applied in various fields was conducted to identify suitable frameworks for highway infrastructure data. Data quality dimensions of the semiotic framework were identified from the literature, and an interview was conducted with the highway infrastructure stakeholders to finalise the data quality dimension. Then, a questionnaire survey identified the critical data quality dimensions for decision-making. Along with the critical dimensions, their level of importance was also identified at each highway infrastructure project’s decision-making levels. The semiotic data quality framework provided a theoretical foundation for developing data quality dimensions to assess subjective data quality. Further research is required to find effective ways to assess current data quality satisfaction at the decision-making levels. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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16 pages, 56317 KiB

Open AccessArticle

Automated Semantic Segmentation of Indoor Point Clouds from Close-Range Images with Three-Dimensional Deep Learning

by Chia-Sheng Hsieh and Xiang-Jie Ruan

Buildings 2023, 13(2), 468; https://doi.org/10.3390/buildings13020468 - 09 Feb 2023

Cited by 2 | Viewed by 2041

Abstract

The creation of building information models requires acquiring real building conditions. The generation of a three-dimensional (3D) model from 3D point clouds involves classification, outline extraction, and boundary regularization for semantic segmentation. The number of 3D point clouds generated using close-range images is smaller and tends to be unevenly distributed, which is not conducive to automated modeling processing. In this paper, we propose an efficient solution for the semantic segmentation of indoor point clouds from close-range images. A 3D deep learning framework that achieves better results is further proposed. A dynamic graph convolutional neural network (DGCNN) 3D deep learning method is used in this study. This method was selected to learn point cloud semantic features. Moreover, more efficient operations can be designed to build a module for extracting point cloud features such that the problem of inadequate beam and column classification can be resolved. First, DGCNN is applied to learn and classify the indoor point cloud into five categories: columns, beams, walls, floors, and ceilings. Then, the proposed semantic segmentation and modeling method is utilized to obtain the geometric parameters of each object to be integrated into building information modeling software. The experimental results show that the overall accuracy rates of the three experimental sections of Area_1 in the Stanford 3D semantic dataset test results are 86.9%, 97.4%, and 92.5%. The segmentation accuracy of corridor 2F in a civil engineering building is 94.2%. In comparing the length with the actual on-site measurement, the root mean square error is found to be ±0.03 m. The proposed method is demonstrated to be capable of automatic semantic segmentation from 3D point clouds with indoor close-range images. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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17 pages, 8500 KiB

Open AccessArticle

Real-Time Monitoring for Effects of Vibration and Temperature of Construction Site on Steel Assembly Bracing of Foundation Pit

by Yingwu Yang, Hangbin Zeng, Xingxi Liu, Bo Yang and Ying Li

Buildings 2023, 13(2), 450; https://doi.org/10.3390/buildings13020450 - 06 Feb 2023

Cited by 1 | Viewed by 1283

Abstract

In this paper, a real-time monitoring system—including vibration acceleration sensors, temperature sensors, and static and dynamic strain sensors—is used to monitor the safety status of a steel assembly bracing in a practical project. It uses 5G wireless networking technology to transmit monitoring data to a cloud server for early warning of abnormal changes and development trends. Real-time monitoring data obtained in a construction site are used as the inputs of the finite element model and the corresponding results of numerical simulation are compared with the results from the real-time monitoring. It can be concluded that: (1) the stress caused by environmental temperature is very significant which can be higher than the initial prestress of the steel assembly bracing; (2) the stress caused by the vertical vibration mainly from construction vehicles is not remarkable, however, the vertical frequency-weighted acceleration of support vibration is relatively large which can affect the sense of safety of engineering technicians on site; (3) the combination of the environmental temperature and vertical vibration does not affect the safety of the steel assembly bracing. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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18 pages, 5187 KiB

Open AccessArticle

Polyethylene (PE) Waste Minimization Study of Cement Mortar with Adding PE Content under Different W/B Ratios

by Keng-Ta Lin, Chang-Chi Hung, Her-Yung Wang and Fu-Lin Wen

Buildings 2022, 12(12), 2117; https://doi.org/10.3390/buildings12122117 - 02 Dec 2022

Cited by 1 | Viewed by 1584

Abstract

Wastes can be effectively used in concrete and the characteristics of concrete can be maintained or enhanced, the economy of waste management can be greatly increased, and the pollution of the earth can be reduced. This study aimed to research the durability of cement mortar prepared using different W/B ratios and different percentages of waste PE content. The cement mortar was mixed with 0%, 1%, 2%, 3%, and 4% waste PE and 20% ground-granulated blast-furnace slag (GGBFS) in W/B ratios of 0.4, 0.5, and 0.6. The results sw that the slump and flow decrease as the waste PE content is increased and increase with increasing W/B ratio, and the setting time is shortened as the waste PE content is increased. In terms of hardened properties, the specimen strength is slightly decreased as the waste PE content is increased, but the hardened properties are better at a later age due to the pozzolanic reaction of slag, which can be verified by microscopic SEM. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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11 pages, 5870 KiB

Open AccessArticle

Real-Time Monitoring for Monolithic Movement of a Heritage Curtilage Using Wireless Sensor Networks

by Lulu Shen, Bo Yang, Yingwu Yang, Xuelin Yang, Wenwei Zhu and Qingzhong Wang

Buildings 2022, 12(11), 1785; https://doi.org/10.3390/buildings12111785 - 25 Oct 2022

Cited by 4 | Viewed by 1438

Abstract

Since monolithic movement is considered a promising technology to relocate historical buildings, corresponding real-time monitoring is of great interest due to the buildings’ age and poor structural integrity. However, the related paperwork and practical applications are still limited. This paper describes a wireless sensor network (WSN)-based strategy as a non-invasive approach to monitor heritage curtilage during monolithic movement. The collected data show that the inclination of the curtilage is almost negligible. With the aid of finite element simulation, it was found that the crack displacement curves changed from −0.02 to 0.07 mm, which is affected by moving direction while the value is not enough to cause structural cracks. The deformation of the steel underpinning beam, which is used to reinforce masonry walls and wooden pillars, is obviously related to the stiffness in different directions. Additionally, the strain variations of the steel chassis, which bear the vertical loads from wooden pillars and masonry walls, are less than 0.04%. This indicates that they are kept within the elastic range during monolithic movement. This work has proved that the WSN-based approach has the potential to be applied as an effective route in real-time monitoring of the monolithic movement of an historic building. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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16 pages, 5874 KiB

Open AccessArticle

Reliability Analysis of RC Slab-Column Joints under Punching Shear Load Using a Machine Learning-Based Surrogate Model

by Lulu Shen, Yuanxie Shen and Shixue Liang

Buildings 2022, 12(10), 1750; https://doi.org/10.3390/buildings12101750 - 20 Oct 2022

Cited by 9 | Viewed by 2043

Abstract

Reinforced concrete slab-column structures, despite their advantages such as architectural flexibility and easy construction, are susceptible to punching shear failure. In addition, punching shear failure is a typical brittle failure, which introduces difficulties in assessing the functionality and failure probability of slab-column structures. Therefore, the prediction of punching shear resistance and corresponding reliability analysis are critical issues in the design of reinforced RC slab-column structures. In order to enhance the computational efficiency of the reliability analysis of reinforced concrete (RC) slab-column joints, a database containing 610 experimental data is used for machine learning (ML) modelling. According to the nonlinear mapping between the selected seven input variables and the punching shear resistance of slab-column joints, four ML models, such as artificial neural network (ANN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) are established. With the assistance of three performance measures, such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), XGBoost is selected as the best prediction model; its RMSE, MAE, and R² are 32.43, 19.51, and 0.99, respectively. Such advantages are also reflected in the comparison with the five empirical models introduced in this paper. The prediction process of XGBoost is visualized by SHapley Additive exPlanation (SHAP); the importance sorting and feature dependency plots of the input variables explain the prediction process globally. Furthermore, this paper adopts Monte Carlo simulation with a machine learning-based surrogate model (ML-MCS) to calibrate the reliability of slab-column joints in a real engineering example. A total of 1,000,000 samples were obtained through random sampling, and the reliability index β of this practical building was calculated by Monte Carlo simulation. Results demonstrate that the target reliability index requirements under design provisions can be achieved. The sensitivity analysis of stochastic variables was then conducted, and the impact of that analysis on structural reliability was deeply examined. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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49 pages, 124728 KiB

Open AccessArticle

An Intelligent Detection Logic for Fan-Blade Damage to Wind Turbines Based on Mounted-Accelerometer Data

by Ming-Hung Hsu and Zheng-Yun Zhuang

Buildings 2022, 12(10), 1588; https://doi.org/10.3390/buildings12101588 - 01 Oct 2022

Cited by 5 | Viewed by 1660

Abstract

Many wind turbines operate in harsh marine or shore environments. This study assists industry by establishing a real-time condition-monitoring and fault-detection system, with rules for recognizing a wind turbine’s abnormal operation mainly caused by different types of fan-blade damage. This system can ensure ideal wind turbine operation by monitoring the health status of the blades, detecting sudden anomalies, and performing maintenance almost in real time. This is especially significant for wind farms in areas subject to frequent natural disasters (e.g., earthquakes and typhoons). Turbines might fail to endure these because the manufacturers have built them according to the standards developed for areas less prone to natural disasters. The system’s rules are established by utilising concepts and methods from data analytics, digital signal processing (DSP) and statistics to analyse data from the accelerometer, which measures the vibration signals in three dimensions on the platform of the wind turbine’s base. The patterns for those cases involving fan-blade damage are found to establish the rules. With the anomalies detected and reported effectively, repairs and maintenance can be carried out on the faulty wind turbines. This enables ‘maintenance by prediction’ actions for unplanned maintenance as a supplement to the ‘predictive maintenance’ tasks for regular planned maintenance. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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47 pages, 2547 KiB

Open AccessArticle

Unravelling the Relations between and Predictive Powers of Different Testing Variables in High Performance Concrete Experiments: The Data-Driven Analytical Methods

by Zheng-Yun Zhuang and Wen-Ten Kuo

Buildings 2022, 12(10), 1545; https://doi.org/10.3390/buildings12101545 - 27 Sep 2022

Cited by 1 | Viewed by 1128

Abstract

This study proposes and applies a systematic data analysis methodology to analyse experimental data for high-performance concrete (HPC) samples with different admixtures for offshore fan foundation grouting materials uses. In contrast with other relevant research, including experimental studies, the materials physics and chemistry studies, or cementitious material portfolio determination studies, this data-driven analysis provides a deep exploration of the experimental variables associated with the test data. To offer complete and in-depth perspectives, several methods are employed for the data analyses, including correlation analysis, cosine similarity analysis, simple linear regression (SLR) modelling, and heat map and heat-based tabularised visualisations; the outcome is a proposed methodology that is easily implementable. The results from these methods are validated using a pairwise comparison approach (PCA) to avoid unnecessary interference between data variables. There are several potential contributions from this work, including insights for cohered groups of variables, techniques for double check and ‘third check’, an established ‘knowledge base’ consisting of 504 SLR predictive models with their effectiveness (significance) and prediction accuracy (data-model fitness) used in practical applications, an alternative visualisations of the results, three data transforms which can be omitted in a future analysis, and three valuable theory-linking perspectives (e.g., for the relationships between destructive and non-destructive tests with respect to the variable categories). The implication that some variables are interchangeable will make future experiments less labour intensive and time consuming for pre-project HPC material testing. Full article

(This article belongs to the Special Issue Applications of (Big) Data Analysis in A/E/C)

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