Towards big data driven construction industry
Tài liệu tham khảo
Chen, 2014, Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, Inform. Sci., 275, 314, 10.1016/j.ins.2014.01.015
Yan, 2020, Data mining in the construction industry: Present status, opportunities, and future trends, Autom. Constr., 119, 10.1016/j.autcon.2020.103331
Snyder, 2018
Lu, 2017, Industry 4.0: A survey on technologies, applications and open research issues, J. Ind. Inf. Integr., 6, 1
Bucchiarone, 2019, Smart construction: remote and adaptable management of construction sites through IoT, IEEE Internet Things Mag., 2, 38, 10.1109/IOTM.0001.1900044
Maddikunta, 2022, Industry 5.0: A survey on enabling technologies and potential applications, J. Ind. Inf. Integr., 26
Hämäläinen, 2019, Industrial applications of big data in disruptive innovations supporting environmental reporting, J. Ind. Inf. Integr., 16
Cheng, 2018, Data and knowledge mining with big data towards smart production, J. Ind. Inf. Integr., 9, 1
Zhou, 2020, Variational LSTM enhanced anomaly detection for industrial big data, IEEE Trans. Ind. Inform., 17, 3469, 10.1109/TII.2020.3022432
Lv, 2017, Next-generation big data analytics: State of the art, challenges, and future research topics, IEEE Trans. Ind. Inform., 13, 1891, 10.1109/TII.2017.2650204
Al-Abassi, 2020, Industrial big data analytics: challenges and opportunities, 37
Wang, 2017, Big data analytics for system stability evaluation strategy in the energy Internet, IEEE Trans. Ind. Inform., 13, 1969, 10.1109/TII.2017.2692775
Wang, 2021, Modeling and monitoring of a multivariate spatio-temporal network system, IISE Trans., 1
Li, 2020, Online distributed IoT security monitoring with multidimensional streaming big data, IEEE Internet Things J., 7, 4387, 10.1109/JIOT.2019.2962788
Rani, 2017, Can sensors collect big data? An energy-efficient big data gathering algorithm for a WSN, IEEE Trans. Ind. Inform., 13, 1961, 10.1109/TII.2017.2656899
Huang, 2021, A projective and discriminative dictionary learning for high-dimensional process monitoring with industrial applications, IEEE Trans. Ind. Inform., 17, 558, 10.1109/TII.2020.2992728
Yin, 2015, Big data for modern industry: challenges and trends [point of view], Proc. IEEE, 103, 143, 10.1109/JPROC.2015.2388958
Yu, 2020, A global manufacturing big data ecosystem for fault detection in predictive maintenance, IEEE Trans. Ind. Inform., 16, 183, 10.1109/TII.2019.2915846
Bilal, 2016, Big Data in the construction industry: A review of present status, opportunities, and future trends, Adv. Eng. Inform., 30, 500, 10.1016/j.aei.2016.07.001
Ismail, 2018, An appraisal into the potential application of big data in the construction industry, Int. J. Built Environ. Sustain., 5, 10.11113/ijbes.v5.n2.274
Wang, 2018, Research on optimization of big data construction engineering quality management based on RNN-LSTM, Complexity, 2018
Zhang, 2019, Reference architecture of common service platform for Industrial Big Data (I-BD) based on multi-party co-construction, Int. J. Adv. Manuf. Technol., 105, 1949, 10.1007/s00170-019-04374-x
Ngo, 2020, Factor-based big data and predictive analytics capability assessment tool for the construction industry, Autom. Constr., 110, 10.1016/j.autcon.2019.103042
You, 2020, Integration of industry 4.0 related technologies in construction industry: A framework of cyber-physical system, IEEE Access, 8, 122908, 10.1109/ACCESS.2020.3007206
Baars, 2008, Management support with structured and unstructured data—an integrated business intelligence framework, Inf. Syst. Manage., 25, 132, 10.1080/10580530801941058
Lv, 2020, BIM big data storage in WebVRGIS, IEEE Trans. Ind. Inform., 16, 2566, 10.1109/TII.2019.2916689
Zhao, 2021, Refined and intelligent management mode of construction project based on BIM and IOT technology, 1
Wang, 2020, IoT-based intelligent construction system for prefabricated buildings: Study of operating mechanism and implementation in China, Appl. Sci., 10, 6311, 10.3390/app10186311
Lu, 2021, Bibliometric analysis and critical review of the research on big data in the construction industry, Eng. Constr. Archit. Manag.
Sisinni, 2018, Industrial internet of things: Challenges, opportunities, and directions, IEEE Trans. Ind. Inform., 14, 4724, 10.1109/TII.2018.2852491
Azhar, 2011, Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry, Leadersh. Manag. Eng., 11, 241, 10.1061/(ASCE)LM.1943-5630.0000127
Chen, 2016, A cloud-based system framework for performing online viewing, storage, and analysis on big data of massive BIMs, Autom. Constr., 71, 34, 10.1016/j.autcon.2016.03.002
Tao, 2019, Make more digital twins, Nature, 573, 490, 10.1038/d41586-019-02849-1
Oesterreich, 2016, Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry, Comput. Ind., 83, 121, 10.1016/j.compind.2016.09.006
Whyte, 2017, How digitizing building information transforms the built environment, Build. Res. Inf., 45, 591, 10.1080/09613218.2017.1324726
Zhang, 2015, BIM-based fall hazard identification and prevention in construction safety planning, Saf. Sci., 72, 31, 10.1016/j.ssci.2014.08.001
Bilal, 2015, Analysis of critical features and evaluation of BIM software: towards a plug-in for construction waste minimization using big data, Int. J. Sustain. Build. Technol. Urban Dev., 6, 211, 10.1080/2093761X.2015.1116415
Garyaev, 2019, Big data technology in construction, 01032
Turner, 2020, Utilizing industry 4.0 on the construction site: Challenges and opportunities, IEEE Trans. Ind. Inform., 17, 746, 10.1109/TII.2020.3002197
Pradhananga, 2013, Automatic spatio-temporal analysis of construction site equipment operations using GPS data, Autom. Constr., 29, 107, 10.1016/j.autcon.2012.09.004
Gong, 2021, Developing a dynamic supervision mechanism to improve construction safety investment supervision efficiency in China: Theoretical simulation of evolutionary game process, Int. J. Environ. Res. Public Health, 18, 3594, 10.3390/ijerph18073594
Lee, 2017, Wearable sensors for monitoring on-duty and off-duty worker physiological status and activities in construction, Autom. Constr., 83, 341, 10.1016/j.autcon.2017.06.012
Choi, 2019, Feasibility analysis of electrodermal activity (EDA) acquired from wearable sensors to assess construction workers’ perceived risk, Saf. Sci., 115, 110, 10.1016/j.ssci.2019.01.022
Winsberg, 2003, Simulated experiments: Methodology for a virtual world, Philos. Sci., 70, 105, 10.1086/367872
Dick, 2019
AI, 2022, The fourth industrial revolution, 6
Tao, 2018, Digital twin in industry: State-of-the-art, IEEE Trans. Ind. Inform., 15, 2405, 10.1109/TII.2018.2873186
Haag, 2018, Digital twin–Proof of concept, Manuf. Lett., 15, 64, 10.1016/j.mfglet.2018.02.006
Evans, 2012, 2018
Wang, 2016, Big data analytics in logistics and supply chain management: Certain investigations for research and applications, Int. J. Prod. Econ., 176, 98, 10.1016/j.ijpe.2016.03.014
Wang, 2008, Enhancing construction quality inspection and management using RFID technology, Autom. Constr., 17, 467, 10.1016/j.autcon.2007.08.005
Malacarne, 2018, Investigating benefits and criticisms of bim for construction scheduling in SMEs: An Italian case study, Int. J. Sustain. Dev. Plan., 13, 139, 10.2495/SDP-V13-N1-139-150
Leitner, 2006, 22
Curry, 2004, Message-oriented middleware, 1
Yongguo, 2019, Message-oriented middleware: A review, 88
Andersen, 1993, Coordinate transformations in the representation of spatial information, Curr. Opin. Neurobiol., 3, 171, 10.1016/0959-4388(93)90206-E
Zhang, 2020, Distribution-aware coordinate representation for human pose estimation, 7093
Gribonval, 2003, Sparse representations in unions of bases, IEEE Trans. Inform. Theory, 49, 3320, 10.1109/TIT.2003.820031
Zhu, 2012, A sparse image fusion algorithm with application to pan-sharpening, IEEE Trans. Geosci. Remote Sens., 51, 2827, 10.1109/TGRS.2012.2213604
Liu, 2019, Joint representation learning for multi-modal transportation recommendation, 1036
Zhai, 2013, Learning cross-media joint representation with sparse and semisupervised regularization, IEEE Trans. Circuits Syst. Video Technol., 24, 965, 10.1109/TCSVT.2013.2276704
Schmitt, 2016, Data fusion and remote sensing: An ever-growing relationship, IEEE Geosci. Remote Sens. Mag., 4, 6, 10.1109/MGRS.2016.2561021
U.S. Department of Defense, 1991
Barton, 1993, ALSCRIPT: a tool to format multiple sequence alignments, Protein Eng. Des. Sel., 6, 37, 10.1093/protein/6.1.37
Pang, 2022, Heterogeneous feature alignment and fusion in cross-modal augmented space for composed image retrieval, IEEE Trans. Multimed., 10.1109/TMM.2022.3208742
Wang, 2008, Aligning temporal data by sentinel events: discovering patterns in electronic health records, 457
García, 2016, Big data preprocessing: methods and prospects, Big Data Anal., 1, 1, 10.1186/s41044-016-0014-0
Sayood, 2017
Bakar, 2009, Building a new taxonomy for data discretization techniques, 132
Taleb, 2017, On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration, IEEE Commun. Surv. Tutor., 19, 1657, 10.1109/COMST.2017.2705720
Sharma, 2016, Expanded cloud plumes hiding Big Data ecosystem, Future Gener. Comput. Syst., 59, 63, 10.1016/j.future.2016.01.003
Dean, 2004
White, 2012
Karau, 2015
Prakash, 2018
Varia, 2014
Bisong, 2019, An overview of google cloud platform services, 7
Jakóbczyk, 2020
Meredith, 2017
Jiang, 2008, Bayesian probabilistic inference for nonparametric damage detection of structures, J. Eng. Mech., 134, 820, 10.1061/(ASCE)0733-9399(2008)134:10(820)
Fernando, 2010, Patterns, heuristics for architectural design support: Making use of evolutionary modelling in design, 283
Mahfouz, 2009
Li, 2014, Application of pattern matching method for detecting faults in air handling unit system, Autom. Constr., 43, 49, 10.1016/j.autcon.2014.03.002
Gong, 2011, Learning and classifying actions of construction workers and equipment using Bag-of-Video-Feature-Words and Bayesian network models, Adv. Eng. Inform., 25, 771, 10.1016/j.aei.2011.06.002
Soibelman, 2002, Data preparation process for construction knowledge generation through knowledge discovery in databases, J. Comput. Civ. Eng., 16, 39, 10.1061/(ASCE)0887-3801(2002)16:1(39)
Kim, 2008, Factor selection for delay analysis using Knowledge Discovery in Databases, Autom. Constr., 17, 550, 10.1016/j.autcon.2007.10.001
Buchheit, 2012, 914
Ahiaga-Dagbui, 2014, Dealing with construction cost overruns using data mining, Constr. Manag. Econ., 32, 682, 10.1080/01446193.2014.933854
Pradhan, 2011, Formalisms for query capture and data source identification to support data fusion for construction productivity monitoring, Autom. Constr., 20, 389, 10.1016/j.autcon.2010.11.009
Bai, 2019, Data mining approach to construction productivity prediction for cutter suction dredgers, Autom. Constr., 105, 10.1016/j.autcon.2019.102833
Rujirayanyong, 2006, A project-oriented data warehouse for construction, Autom. Constr., 15, 800, 10.1016/j.autcon.2005.11.001
Liao, 2008, Data mining for occupational injuries in the Taiwan construction industry, Saf. Sci., 46, 1091, 10.1016/j.ssci.2007.04.007
Wu, 2019, New automated BIM object classification method to support BIM interoperability, J. Comput. Civ. Eng., 33, 10.1061/(ASCE)CP.1943-5487.0000858
Romero-Jarén, 2021, Automatic segmentation and classification of BIM elements from point clouds, Autom. Constr., 124, 10.1016/j.autcon.2021.103576
Yamamoto, 2011, A genetic algorithm based form-finding for tensegrity structure, Procedia Eng., 14, 2949, 10.1016/j.proeng.2011.07.371
Mehanna, 2013, Resilient structures through machine learning and evolution, 319, 10.52842/conf.acadia.2013.319
Chen, 2011, Using BIM model and genetic algorithms to optimize the crew assignment for construction project planning, Int. J. Technol., 179
Shin, 2011, Simulation model incorporating genetic algorithms for optimal temporary hoist planning in high-rise building construction, Autom. Constr., 20, 550, 10.1016/j.autcon.2010.11.021
Hwang, 2009, Dynamic regression models for prediction of construction costs, J. Constr. Eng. Manag., 135, 360, 10.1061/(ASCE)CO.1943-7862.0000006
Zhang, 2016, Structural health monitoring of Shanghai Tower during different stages using a Bayesian approach, Struct. Control Health Monit., 23, 1366, 10.1002/stc.1840
Kang, 2019, Predicting types of occupational accidents at construction sites in Korea using random forest model, Saf. Sci., 120, 226, 10.1016/j.ssci.2019.06.034
Kale, 2020, Identifying factors that contribute to severity of construction injuries using logistic regression model, Tek. Dergi, 31, 9919, 10.18400/tekderg.470633
Liu, 2011, Application of genetic algorithm-support vector machine (GA-SVM) for damage identification of bridge, Int. J. Comput. Intell. Appl., 10, 383, 10.1142/S1469026811003215
Jahanshahi, 2012, Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures, Autom. Constr., 22, 567, 10.1016/j.autcon.2011.11.018
Asadi, 2015, A machine learning approach for predicting delays in construction logistics, Int. J. Adv. Logist., 4, 115, 10.1080/2287108X.2015.1059920
Ardeshir, 2018, A prioritization model for hse risk assessment using combined failure mode, effect analysis, and fuzzy inference system: A case study in iranian construction industry, Int. J. Eng., 31, 1487
Pietrzyk, 2015, A systemic approach to moisture problems in buildings for mould safety modelling, Build. Environ., 86, 50, 10.1016/j.buildenv.2014.12.013
Cha, 2016, Vision-based detection of loosened bolts using the Hough transform and support vector machines, Autom. Constr., 71, 181, 10.1016/j.autcon.2016.06.008
Dehestani, 2011
Kim, 2020, Proximity prediction of mobile objects to prevent contact-driven accidents in co-robotic construction, J. Comput. Civ. Eng., 34, 10.1061/(ASCE)CP.1943-5487.0000899
Kamari, 2021, Vision-based volumetric measurements via deep learning-based point cloud segmentation for material management in jobsites, Autom. Constr., 121, 10.1016/j.autcon.2020.103430
Rashid, 2019, Times-series data augmentation and deep learning for construction equipment activity recognition, Adv. Eng. Inform., 42, 10.1016/j.aei.2019.100944
El-Gohary, 2017, Engineering approach using ANN to improve and predict construction labor productivity under different influences, J. Constr. Eng. Manag., 143, 10.1061/(ASCE)CO.1943-7862.0001340
2021, Measuring and benchmarking the productivity of excavators in infrastructure projects: A deep neural network approach, Autom. Constr., 124
Fang, 2018, A deep learning-based method for detecting non-certified work on construction sites, Adv. Eng. Inform., 35, 56, 10.1016/j.aei.2018.01.001
Juszczyk, 2019, Forecasting of sports fields construction costs aided by ensembles of neural networks, J. Civ. Eng. Manag., 25, 715, 10.3846/jcem.2019.10534
Slaton, 2020, Construction activity recognition with convolutional recurrent networks, Autom. Constr., 113, 10.1016/j.autcon.2020.103138
Zhang, 2019, Construction site accident analysis using text mining and natural language processing techniques, Autom. Constr., 99, 238, 10.1016/j.autcon.2018.12.016
Tixier, 2017, Construction safety clash detection: identifying safety incompatibilities among fundamental attributes using data mining, Autom. Constr., 74, 39, 10.1016/j.autcon.2016.11.001
Wu, 2019, Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset, Autom. Constr., 106, 10.1016/j.autcon.2019.102894
Ayhan, 2019, Predicting the outcome of construction incidents, Saf. Sci., 113, 91, 10.1016/j.ssci.2018.11.001
Zhao, 2019, Deep learning for risk detection and trajectory tracking at construction sites, IEEE Access, 7, 30905, 10.1109/ACCESS.2019.2902658
Lowe, 2006, Predicting construction cost using multiple regression techniques, J. Constr. Eng. Manag., 132, 750, 10.1061/(ASCE)0733-9364(2006)132:7(750)
Rafiei, 2018, Novel machine-learning model for estimating construction costs considering economic variables and indexes, J. Constr. Eng. Manag., 144, 10.1061/(ASCE)CO.1943-7862.0001570
Bayram, 2016, Comparison of multi layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: the case of Turkey, J. Civ. Eng. Manag., 22, 480, 10.3846/13923730.2014.897988
Cheng, 2012, A novel time-depended evolutionary fuzzy SVM inference model for estimating construction project at completion, Eng. Appl. Artif. Intell., 25, 744, 10.1016/j.engappai.2011.09.022
Yu, 2016, The knowledge modeling system of ready-mixed concrete enterprise and artificial intelligence with ANN-GA for manufacturing production, J. Intell. Manuf., 27, 905, 10.1007/s10845-014-0923-6
Business Application Research Center (BARC), 2016
McKinsey & Company, 2016
Dodge Data & Analytics, 2019
Barata, 2019, Safety is the new black: the increasing role of wearables in occupational health and safety in construction, 526
Aslam, 2020, Review of construction and demolition waste management in China and USA, J. Environ. Manag., 264, 10.1016/j.jenvman.2020.110445
Deng, 2008, A study of construction and demolition waste management in Hong Kong, 1
Moselhi, 2009, Optimization of earthmoving operations in heavy civil engineering projects, J. Constr. Eng. Manag., 135, 948, 10.1061/(ASCE)0733-9364(2009)135:10(948)
Van Tam, 2018, Factors affecting labour productivity of construction worker on construction site: A case of Hanoi, J. Sci. Technol. Civ. Eng. (STCE)-HUCE, 12, 127, 10.31814/stce.nuce2018-12(5)-13
Li, 2022, Detection and identification of cyber and physical attacks on distribution power grids with pvs: An online high-dimensional data-driven approach, IEEE J. Emerg. Sel. Top. Power Electron., 10, 1282, 10.1109/JESTPE.2019.2943449
Lazer, 2014, The parable of Google Flu: traps in big data analysis, Science, 343, 1203, 10.1126/science.1248506
Fan, 2014, Challenges of big data analysis, Natl. Sci. Rev., 1, 293, 10.1093/nsr/nwt032
Wang, 2021, Blockchain-based reliable and efficient certificateless signature for IIoT devices, IEEE Trans. Ind. Inform.
Sun, 2018, Cyber security of a power grid: State-of-the-art, Int. J. Electr. Power Energy Syst., 99, 45, 10.1016/j.ijepes.2017.12.020
Li, 2019, Enhanced cyber-physical security in internet of things through energy auditing, IEEE Internet Things J., 6, 5224, 10.1109/JIOT.2019.2899492
Zhao, 2021, A federated learning framework for detecting false data injection attacks in solar farms, IEEE Trans. Power Electron., 37, 2496, 10.1109/TPEL.2021.3114671
Lu, 2019, Blockchain and federated learning for privacy-preserved data sharing in industrial IoT, IEEE Trans. Ind. Inform., 16, 4177, 10.1109/TII.2019.2942190
Li, 2018, 5G Internet of Things: A survey, J. Ind. Inf. Integr., 10, 1
Lu, 2020, 6G: A survey on technologies, scenarios, challenges, and the related issues, J. Ind. Inf. Integr., 19
Chen, 2019, Survey of cross-technology communication for IoT heterogeneous devices, IET Commun., 13, 1709, 10.1049/iet-com.2018.6069