International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 2 | Year 2026 | Article Id. IJETT-V74I2P117 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I2P117

Digital Twins in the Construction Industry for Continuous Process Improvement: A Systematic Literature Review


Fernández Paucar Carlos Andrés

Received Revised Accepted Published
29 Sep 2025 11 Jan 2026 20 Jan 2026 14 Feb 2026

Citation :

Fernández Paucar Carlos Andrés, "Digital Twins in the Construction Industry for Continuous Process Improvement: A Systematic Literature Review," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 2, pp. 235-244, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I2P117

Abstract

This systematic literature review analyzes fifty peer-reviewed studies that address the use of digital twin technology in the construction industry. The objective of the review is to examine how digital twins contribute to process improvement and productivity enhancement when compared with conventional monitoring and control approaches. The study selection process was conducted using the PRISMA methodology, supported by the PICOC framework, and focused on publications indexed in the Scopus database. The analyzed literature reports that the implementation of digital twins enables measurable gains in operational productivity-frequently reported at around 30%-and contributes to error reduction on construction sites through real-time data integration, advanced simulation models, and data-informed decision-making. Despite these reported benefits, adoption in the construction sector remains limited. Common barriers identified include insufficient digital competencies, regulatory and organizational constraints, and the complexity associated with managing large volumes of heterogeneous data. Based on the reviewed evidence, this study highlights the need for coordinated digital transformation strategies, greater interoperability with technologies such as Building Information Modeling (BIM) and cyber-physical systems, and clearer implementation guidelines to strengthen the role of digital twins as a practical and sustainable tool within construction processes.

Keywords

Construction industry, Digital twin, Productivity, Continuous improvement, Technology.

References

[1] De-Graft Joe Opoku et al., “Digital Twin Application in the Construction Industry: A Literature Review,” Journal of Building Engineering, vol. 40, 2021.
[
CrossRef] [Google Scholar] [Publisher Link]

[2] Abiola A. Akanmu, Chimay J. Anumba, and Omobolanle O. Ogunseiju, “Towards Next Generation Cyber-Physical Systems and Digital Twins for Construction,” Journal of Information Technology in Construction, vol. 26, pp. 505-525, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Tran Duong Nguyen, and Sanjeev Adhikari, “The Role of BIM in Integrating Digital Twin in Building Construction: A Literature Review,” Sustainability, vol. 15, no. 13, pp. 1-26, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Ashtarout Ammar et al., “Digital Twins in the Construction Industry: A Perspective of Practitioners and Building Authority,” Frontiers in Built Environment, vol. 8, pp. 1-23, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Pedro Mêda et al., “Incremental Digital Twin Conceptualisations Targeting Data-Driven Circular Construction,” Buildings, vol. 11, no. 11, pp. 1-27, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Matthew J. Page, “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews,” Rev Panam Salud Publica, vol. 46, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Yang Wen, and Fangliang Yu, “Construction of Wireless Underground Footwork Mobile Training and Monitoring Sensor Network in Venues of Major Sports Events,” Journal of Sensors, vol. 2021, no. 1, pp. 1-11, 2021.
[
CrossRef] [Google Scholar] [Publisher Link]

[8] Gaoyang Liang, Peng Cao, and Yang Liu, “Optimization and Simulation of Labor Resource Management Information Platform based on Internet of Things,” Wireless Communications and Mobile Computing, vol. 2021, no. 1, pp. 1-11, 2021.
[
CrossRef] [Google Scholar] [Publisher Link]

[9]  John N. Hodul et al., “Poly(5-Carboxyindole)-β-Cyclodextrin Composite Material for Enhanced Formaldehyde Gas Sensing,” Journal of Materials Science, vol. 57, no. 24, pp. 11460-11474, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Menglong Li et al., “Establishment of Web-Based Digital Twin System for Truss Gantry Crane,” IEEE Access, vol. 11, pp. 146282-146296, 2023.
[
CrossRef] [Google Scholar] [Publisher Link]

[11]  Mohammadali Farahpoor, Oscar Esparza, and Miguel C. Soriano, “Comprehensive IoT-Driven Fleet Management System for Industrial Vehicles,” IEEE Access, vol. 12, pp. 193429-193444, 2024.
[
CrossRef] [Google Scholar] [Publisher Link]

[12] Jinqiang Bi et al., “Research on the Construction of a Digital Twin System for the Long-Term Service Monitoring of Port Terminals,” Journal of Marine Science and Engineering, vol. 12, no. 7, pp. 1-16, 2024.
[
CrossRef] [Google Scholar] [Publisher Link]

[13] Haksun Kim et al., “Bluetooth Load-Cell-Based Support-Monitoring System for Safety Management at a Construction Site,” Sensors, vol. 22, no. 10, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[14] G.S. Arun Kumar et al., “A Comprehensive Approach to Real-time Site Monitoring and Risk Assessment in Construction Settings using Internet of Things and Artificial Intelligence,” SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 8, pp. 112-126, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Bin Yang et al., “Semantic Segmentation-Based Framework for Concrete Pouring Progress Monitoring using Multiple Surveillance Cameras,” Developments in the Built Environment, vol. 16, pp. 1-16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Yogesh Gautam, and Houtan Jebelli, “Autoencoder-Based Photoplethysmography Signal Reliability Enhancement in Construction Health Monitoring,” Automation in Construction, vol. 165, pp. 1-14, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Douglas Nascimento et al., “Current Sensor Optimization based on Simulated Transfer Function under Partial Discharge Pulses,” Sensors and Actuators A: Physical, vol. 329, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Łukasz Bednarski et al., “New Hydraulic Sensor for Distributed and Automated Displacement Measurements with Temperature Compensation System,” Sensors, vol. 21, no. 14, pp. 1-20, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Piotr Sowiński et al., “Frugal Heart Rate Correction Method for Scalable Health and Safety Monitoring in Construction Sites,” Sensors, vol. 23, no. 14, pp. 1-19, 2023.
[
CrossRef] [Google Scholar] [Publisher Link]

[20] Rajitha Ranasinghe, Arooran Sounthararajah, and Jayantha Kodikara, “An Intelligent Compaction Analyzer: A Versatile Platform for Real-Time Recording, Monitoring, and Analyzing of Road Material Compaction,” Sensors, vol. 23, no. 17, pp. 1-24, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Song Shi et al., “Recognition System of Human Fatigue State based on Hip Gait Information in Gait Patterns,” Electronics, vol. 11, no. 21, pp. 1-11, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[22] Jing Hung Chan, and Chee Yong Lau, “Enhancement of Jaibot: Developing Safety and Monitoring Features for Jaibot using IoT Technologies,” International Journal of Technology, vol. 14, no. 6, pp. 1309-1319, 2023.
[
CrossRef] [Google Scholar] [Publisher Link]

[23] Aydin Jadidi et al., “Beam Offset Detection in Laser Stake Welding of Tee Joints using Machine Learning and Spectrometer Measurements,” Sensors, vol. 22, no. 10, pp. 1-15, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[24] Cheng Chang et al., “Rapid Quality Control for Recycled Coarse Aggregates (RCA) Streams: Multi-Sensor Integration for Advanced Contaminant Detection,” Computers in Industry, vol. 164, pp. 1-14, 2025.
[
CrossRef] [Google Scholar] [Publisher Link]

[25] Wenping Wu, and Biao Lu, “Construction of Wireless Sensor Network Video Surveillance System for Multimedia Classroom Education and Teaching under 5G Communication Network,” Journal of Sensors, vol. 2022, pp. 1-12, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[26] Celso O. Barcelos et al., “Integration of Payload Sensors to Enhance UAV-Based Spraying,” Drones, vol. 8, no. 9, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Namhoon Ha, Han-Seung Lee, and Songjun Lee, “Development of a Wireless Corrosion Detection System for Steel-Framed Structures using Pulsed Eddy Currents,” Sensors, vol. 21, no. 24, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[28] Tiago Rabelo Chaves et al., “Application Study in the Field of Solutions for the Monitoring Distribution Transformers of the Overhead Power Grid,” Energies, vol. 14, no. 19, pp. 1-15, 2021.
[
CrossRef] [Google Scholar] [Publisher Link]

[29] Lauren C. Tindale et al., “Wearable Biosensors in the Workplace: Perceptions and Perspectives,” Frontiers in Digital Health, vol. 4, pp. 1-11, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[30] Mohammadali Khazen, Mazdak Nik-Bakht, and Osama Moselhi, “Monitoring Workers on Indoor Construction Sites using Data Fusion of Real-Time Worker’s Location, Body Orientation, and Productivity State,” Automation in Construction, vol. 160, pp. 1-21, 2024.
[
CrossRef] [Google Scholar] [Publisher Link]

[31] Caterina Amici et al., “Framework for Computerizing the Processes of a Job and Automating the Operational Management on Site-A Case Study of Demolition and Reconstruction Construction Site,” Buildings, vol. 12, no. 6, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[32] Lu Zhang et al., “Characterization of Damage Progress in the Defective Grouted Sleeve Connection using Combined Acoustic Emission and Ultrasonics,” Sensors, vol. 22, no. 21, pp. 1-18, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[33] Yelbek Utepov et al., “Performance of a Wireless Sensor Adopted in Monitoring of Concrete Strength,” International Journal of Geomate, vol. 23, no. 95, pp. 73-80, 2022.
[
Google Scholar] [Publisher Link]

[34] Elham Mahamedi et al., “Automating Excavator Productivity Measurement using Deep Learning,” Proceedings of the Institution of Civil Engineers Smart Infrastructure and Construction, vol. 174, no. 4, pp. 121-133, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[35] Tan Kai Noel Quah et al., “Real Time Assessment of Smart Concrete Inspection with Piezoelectric Sensors,” Electronics, vol. 12, no. 18, pp. 1-22, 2023.
[
CrossRef] [Google Scholar] [Publisher Link]

[36] Alireza Ansaripour et al., “ViPER+: Vehicle Pose Estimation using Ultra-Wideband Radios for Automated Construction Safety Monitoring,” Applied Sciences, vol. 13, no. 3, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[37] Jianwei Wang et al., “A Tubular Flexible Triboelectric Nanogenerator with a Superhydrophobic Surface for Human Motion Detecting,” Sensors, vol. 21, no. 11, pp. 1-11, 2021.
[
CrossRef] [Google Scholar] [Publisher Link]

[38] Shubham Bhokare et al., “Smart Construction Scheduling Monitoring using YOLOv3-Based Activity Detection and Classification,” Journal of Information Technology in Construction, vol. 27, pp. 240-252, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[39] Zhiming Ding et al., “An Internet of Things based Scalable Framework for Disaster Data Management,” Journal of Safety Science and Resilience, vol. 3, no. 2, pp. 136-152, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[40] Chen Chen et al., “Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection,” Buildings, vol. 12, no. 12, pp. 1-12, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[41] Baydaa Hashim Mohammed et al., “Nexus between Building Information Modeling and Internet of Things in the Construction Industries,” Applied Sciences, vol. 12, no. 20, pp. 1-22, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[42] M. Chen, and Alfredo I. Hernández, “Towards an Explainable Model for Sepsis Detection based on Sensitivity Analysis,” IRBM, vol. 43, no. 1, pp. 75-86, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[43] Tushar Bansal et al., “Embedded Piezo-Sensor-Based Automatic Performance Monitoring of Chloride-Induced Corrosion in Alkali-Activated Concrete,” Sustainability, vol. 14, no. 19, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[44] Anastasios Drougkas et al., “Design of a Smart Lime Mortar with Conductive Micro and Nano Fillers for Structural Health Monitoring,” Construction and Building Materials, vol. 367, pp. 1-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[45] Harrison Fugate, and Hani Alzraiee, “Quantitative Analysis of Construction Labor Acceptance of Wearable Sensing Devices to Enhance Workers’ Safety,” Results in Engineering, vol. 17, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[46] Roshan Panahi et al., “Bottleneck Detection in Modular Construction Factories using Computer Vision,” Sensors, vol. 23, no. 8, pp. 1-23, 2023.
[
CrossRef] [Google Scholar] [Publisher Link]

[47] Min Deng, Carol C. Menassa, and Vineet R. Kamat, “From BIM to Digital Twins: A Systematic Review of the Evolution of Intelligent Building Representations in the AEC-FM Industry,” Journal of Information Technology in Construction, vol. 26, pp. 58-83, 2021.
[
CrossRef] [Google Scholar] [Publisher Link]

[48] Tae Wook Kang, and Yunjeong Mo, “A Comprehensive Digital Twin Framework for Building Environment Monitoring with Emphasis on Real-Time Data Connectivity and Predictability,” Developments in the Built Environment, vol. 17, pp. 1-12, 2024.
[
CrossRef] [Google Scholar] [Publisher Link]

[49] Kinzo Kishida et al., “Distributed Optical Fiber Sensors for Monitoring of Civil Engineering Structures,” Sensors, vol. 22, no. 12, pp. 1-18, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

[50] Nabeeha Ehsan Mughal et al., “EEG-fNIRS-Based Hybrid Image Construction and Classification using CNN-LSTM,” Frontiers in Neurorobotics, vol. 16, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[51] Michele Scarpiniti et al., “Deep Belief Network based Audio Classification for Construction Sites Monitoring,” Expert Systems with Applications, vol. 177, pp. 1-33, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[52] Daigo Terutsuki et al., “Real-Time Odor Concentration and Direction Recognition for Efficient Odor Source Localization using a Small Bio-Hybrid Drone,” Sensors and Actuators B: Chemical, vol. 339, pp. 1-10, 2021.
[
CrossRef] [Google Scholar] [Publisher Link]

[53] Zifeng Qiu, Huihui Bai, and Taoyi Chen, “Special Vehicle Detection from UAV Perspective via YOLO-GNS based Deep Learning Network,” Drones, vol. 7, no. 2, pp. 1-18, 2023.
[
CrossRef] [Google Scholar] [Publisher Link]

[54] Yue Gong et al., “Wearable Acceleration-Based Action Recognition for Long-Term and Continuous Activity Analysis in Construction Site,” Journal of Building Engineering, vol. 52, 2022.
[
CrossRef] [Google Scholar] [Publisher Link]

Ruihan Bai et al., “Automated Construction Site Monitoring Based on Improved YOLOv8-seg Instance Segmentation Algorithm,” IEEE Access, vol. 11, pp. 139082-139096, 2023.
       [
CrossRef] [Google Scholar] [Publisher Link]