Open Access Research Article Issue
Intelligent ventilation-on-demand control system for the construction of underground tunnel complex
Journal of Intelligent Construction 2024, 2 (2): 9180032
Published: 27 May 2024
Abstract PDF (6.5 MB) Collect

Traditional ventilation methods consume excessive energy but still fail to meet requirements in underground tunnel group construction. Thus, a closed-loop intelligent control system for ventilation-on-demand (VOD) was developed. To address dynamic changes in ventilation load and reduce energy consumption, firstly, the developed system calculates the real-time ventilation load and establishes a ventilation-network-based control mode to represent the ventilation system structure. The deep deterministic policy gradient (DDPG) method was then employed for the closed-loop control ensuring the required air volume in each branch of tunnel groups while minimizing energy consumption. After that, the developed closed-loop intelligent ventilation control system encompasses comprehensive perception, real analysis, real-time control, and continuous optimization. This system treats decision-making, control, and feedback as subsystems that reflect the adaptability between ventilation efficiency, construction progress, and power consumption. Finally, the end-edge-cloud-based software of the system was developed to enable remote control and display on large screens, personal computers (PCs), and mobile applications (Apps) to ensure precise and timely operation. The system was employed in tunnel group under construction at the Xulong Hydropower Station in Southwestern China, and the obtained results validate its advanced closed-loop control based on reinforcement learning (RL) and confirm its feasibility in engineering practice.

Open Access Editorial Issue
Celebrating the one-year anniversary of Journal of Intelligent Construction
Journal of Intelligent Construction 2024, 2 (1): 9180029
Published: 11 March 2024
Abstract PDF (633.4 KB) Collect
Open Access Research Article Issue
Full participation flat closed-loop safety management method for offshore wind power construction sites
Journal of Intelligent Construction 2023, 1 (1): 9180006
Published: 13 April 2023
Abstract PDF (16.5 MB) Collect

This study aims to develop a full participation flat closed-loop (FPFCL) safety management method for offshore wind power (OWP) construction sites. People participation in safety management is improved by giving rewards based on evolutionary game theory. The method avoids management deficiencies due to information loss by reducing redundant management hierarchies and establishing point-to-point communication. The closed-loop mechanism ensures that a safety hazard is timely rectified. Meanwhile, an OWP safety management system (OWPsafety) is developed based on the social media platform (WeChat). The functions of the system include safety hazard report, processing center, and personal center. The software runs on smartphones and allows all stakeholders to participate in safety management, leveraging the advantages of social media in the sharing of knowledge. The benefits of this systematic approach include the elimination of time and space isolation, the interconnection between different construction parties, and the promotion of participation. The proposed method and system were applied to four OWP construction sites. The monthly rectification rate of safety hazards is maintained at more than 91%. Successful on-site tests demonstrated that the method and system can effectively solve the safety management challenges in OWP projects.

Open Access Editorial Issue
Journal of Intelligent Construction: A new platform for sharing multidisciplinary research on emerging construction technologies
Journal of Intelligent Construction 2023, 1 (1): 9180008
Published: 11 April 2023
Abstract PDF (587.6 KB) Collect
Open Access Issue
An ANN-Based Short-Term Temperature Forecast Model for Mass Concrete Cooling Control
Tsinghua Science and Technology 2023, 28 (3): 511-524
Published: 13 December 2022
Abstract PDF (17.5 MB) Collect

Concrete temperature control during dam construction (e.g., concrete placement and curing) is important for cracking prevention. In this study, a short-term temperature forecast model for mass concrete cooling control is developed using artificial neural networks (ANN). The development workflow for the forecast model consists of data integration, data preprocessing, model construction, and model application. More than 80 000 monitoring samples are collected by the developed intelligent cooling control system in the Baihetan Arch Dam, which is the largest hydropower project in the world under construction. Machine learning algorithms, including ANN, support vector machines, long short-term memory networks, and decision tree structures, are compared in temperature prediction, and the ANN is determined to be the best for the forecast model. Furthermore, an ANN framework with two hidden layers is determined to forecast concrete temperature at intervals of one day. The root mean square error of the forecast precision is 0.15 C on average. The application on concrete blocks verifies that the developed ANN-based forecast model can be used for intelligent cooling control during mass concrete construction.

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