Examination of Lightning-Induced Damage in Timber

The ancient Chinese architectures were constructed using timber as the main building material. Considering that the lightning strike is the primary natural cause of damage to ancient building, the lightning strike damage mechanism of ancient building timber and the related influencing factors are investigated using the representative timber materials from the ancient building. The burning of timber was mainly caused by the heat of lightning arc. The splitting and damage pit of timber were mainly caused by the mechanical force generated by the temperature rise of the injected by lightning current and air shock wave effects of the lightning. These ways all played in different roles under different conditions. The higher the water content of timber was, the easier it was to crack, and the greater the damage depth and the larger the damage area were. It was easy to burn for the dry timber or the loose timber with low density, but it was difficult for the thick timber. When the wood was too thin, the lightning air shock wave could cause damage. This research may provide reference for protection of ancient timber architecture from possible damage caused by lightning

Multi-source data integration and multi-scale modeling framework for progressive prediction of complex geological interfaces in tunneling

A reliable geological model plays a fundamental role in the efficiency and safety of mountain tunnel construction. However, regional models based on limited survey data represent macroscopic geological environments but not detailed internal geological characteristics, especially at tunnel portals with complex geological conditions. This paper presents a comprehensive methodological framework for refined modeling of the tunnel surrounding rock and subsequent mechanics analysis, with a particular focus on natural space distortion of hard-soft rock interfaces at tunnel portals. The progressive prediction of geological structures is developed considering multi-source data derived from the tunnel survey and excavation stages. To improve the accuracy of the models, a novel modeling method is proposed to integrate multi-source and multi-scale data based on data extraction and potential field interpolation. Finally, a regional-scale model and an engineering-scale model are built, providing a clear insight into geological phenomena and supporting numerical calculation. In addition, the proposed framework is applied to a case study, the Long-tou mountain tunnel project in Guangzhou, China, where the dominant rock type is granite. The results show that the data integration and modeling methods effectively improve model structure refinement. The improved model's calculation deviation is reduced by about 10% to 20% in the mechanical analysis. This study contributes to revealing the complex geological environment with singular interfaces and promoting the safety and performance of mountain tunneling

Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks

To enable large-scale and efficient deployment of artificial intelligence (AI), the combination of AI and edge computing has spawned Edge Intelligence, which leverages the computing and communication capabilities of end devices and edge servers to process data closer to where it is generated. A key technology for edge intelligence is the privacy-protecting machine learning paradigm known as Federated Learning (FL), which enables data owners to train models without having to transfer raw data to third-party servers. However, FL networks are expected to involve thousands of heterogeneous distributed devices. As a result, communication efficiency remains a key bottleneck. To reduce node failures and device exits, a Hierarchical Federated Learning (HFL) framework is proposed, where a designated cluster leader supports the data owner through intermediate model aggregation. Therefore, based on the improvement of edge server resource utilization, this paper can effectively make up for the limitation of cache capacity. In order to mitigate the impact of soft clicks on the quality of user experience (QoE), the authors model the user QoE as a comprehensive system cost. To solve the formulaic problem, the authors propose a decentralized caching algorithm with federated deep reinforcement learning (DRL) and federated learning (FL), where multiple agents learn and make decisions independentl

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning

This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet connection is not ideal for critical operations. Therefore, edge computing, as a distributed computing architecture, moves the location of processing applications, data and services from the central node of the network to the logical edge node of the network to reduce the dependence on cloud processing and analysis of data, and achieve near-end data processing and analysis. The combination of iot and edge computing can reduce latency, improve efficiency, and enhance security, thereby driving the development of intelligent systems. The paper also introduces the development of iot monitoring and control technology, the application of edge computing in iot monitoring and control, and the role of machine learning in data analysis and fault detection. Finally, the application and effect of intelligent Internet of Things monitoring and control system in industry, agriculture, medical and other fields are demonstrated through practical cases and experimental studies

Current and Future Trends of Resource Misallocation in the Construction Industry: A Bibliometric Review with Grounded Theory

[EN] Resource misallocation (RM) refers to the existence of marginal output inequalities between different industries or companies in an economy. Prior studies of RM have mostly focused on effect analysis, construction industry structure upgrades, and organization management. However, these studies have been fragmented and unrelated. This paper analyzes the status quo, consequences, and emerging trends of RM research at the macroscopic level based on current problems and with the aim of exploring potential solutions. Drawing on grounded theory, a qualitative analysis using text-mining is used to analyze the characteristics of 124 RM-related papers. The results more comprehensively and systematically reveal that current RM research encompasses four major dimensions of sources and concepts, misallocation degree measurement and characterization, focused issues (field), and RM research deficiencies. Methods for measuring RM have also been developed from the simple proportional method to current mainstream methods (e.g., growth rate decomposition and variant substitution). We conclude that, in order for this discipline to thrive and effectively reduce RM, future research into RM should focus on core categories, especially the reform of market-oriented factors, transformation of government functions, construction industrial structure adjustment, and methods of income distribution. This systematic review provides a discipline oversight and uncovers necessary and potential research directionsThis research is supported by the National Social Science Fund projects (No. 20BJY010); National Social Science Fund Post-financing projects (No. 19FJYB017); Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund (No. 71942006); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (No. 2018-GH-006 and No. 2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No. E-GKRWJC20202914).Zhang, J.; Dong, F.; Ballesteros-Pérez, P.; Li, H.; Skitmore, M. (2022). Current and Future Trends of Resource Misallocation in the Construction Industry: A Bibliometric Review with Grounded Theory. Buildings. 12(10):1-19. https://doi.org/10.3390/buildings12101731119121