With the development of the next generation of information technology, an increasing amount of attention is being paid to smart residential spaces, including smart cities, smart buildings, and smart homes. Building indoor safety intelligence is an important research topic. However, current indoor safety management methods cannot comprehensively analyse safety data, owing to a poor combination of safety management and building information. Additionally, the judgement of danger depends significantly on the experience of the safety management staff. In this study, digital twins (DTs) are introduced to building indoor safety management. A framework for an indoor safety management system based on DT is proposed which exploits the Internet of Things (IoT), building information modelling (BIM), the Internet, and support vector machines (SVMs) to improve the level of intelligence for building indoor safety management. A DT model (DTM) is developed using BIM integrated with operation information collected by IoT sensors. The trained SVM model is used to automatically obtain the types and levels of danger by processing the data in the DTM. The Internet is a medium for interactions between people and systems. A building in the bobsleigh and sled stadium for the Beijing Winter Olympics is considered as an example; the proposed system realises the functions of the scene display of the operation status, danger warning and positioning, danger classification and level assessment, and danger handling suggestions.

The safety of prestressed steel structures in service has been studied widely. However, traditional safety assessment methods for prestressed steel structures involve few sample points, do not provide accurate predictions, and consume substantial human and material resources. The digital twin technology can be used to monitor the structural behavior, state, and activity of a steel structure throughout its life cycle, which is equivalent to performing a safety assessment of the structure. The purpose of this study is to establish a digital twin multidimensional model of prestressed steel structures. Based on this model, the support vector machine and prediction model are trained using the relevant structural history data, and the safety risk level of the structure is then predicted based on the measured data. Finally, a proportional reduction model of the wheel-spoke cable truss structure is used to verify the feasibility of the proposed method. The results show that digital twin technology can achieve real-time monitoring of prestressed steel structures in use and can provide timely predictions of the safety level. This represents a new method for the safety risk assessment of prestressed steel structures.

In this study, to address the problems of multiple dimensions, large scales, complex tension resource scheduling, and strict quality control requirements in the tensioning process of cables in prestressed steel structures, the technical characteristics of digital twins (DTs) and artificial intelligence (AI) are analyzed. An intelligent tensioning of prestressed cables method driven by the integration of DTs and AI is proposed. Based on the current research status of cable tensioning and DTs, combined with the goal of intelligent tensioning, a fusion mechanism for DTs and AI is established and their integration to drive intelligent tensioning of prestressed cables technology is analyzed. In addition, the key issues involved in the construction of an intelligent control center driven by the integration of DTs and AI are discussed. By considering the construction elements of space and time dimensions, the tensioning process is controlled at multiple levels, thereby realizing the intelligent tensioning of prestressed cables. Driven by intelligent tensioning methods, the safety performance evaluation of the intelligent tensioning process is analyzed. Combined with sensing equipment and intelligent algorithms, a high-fidelity twin model and three-dimensional integrated data model are constructed to realize closed-loop control of the intelligent tensioning safety evaluation. Through the study of digital twins and artificial intelligence fusion to drive the intelligent tensioning method for prestressed cables, this study focuses on the analysis of the intelligent evaluation of safety performance. This study provides a reference for fusion applications with DTs and AI in intelligent tensioning of prestressed cables.

No abstract

In order to study the correlation between parameters and performance indicators in the cutting process, the importance of different parameters in performance indicators should be determined. In the present study, the side milling process of titanium alloy by the end milling cutter is considered the research object, and analytic hierarchy process and grey-fuzzy evaluation method are used to evaluate the importance of tool geometric parameters and operating parameters on tool wear rate and material removal rate obtained by FEM method. It is found that applying the average method to remove the parameter level makes each parameter achieve the same result. Therefore, this method should be combined with other data processing methods to resolve the above problem. Finally, the range analysis method is applied to obtain the optimal parameter level of different parameters for each performance indicator. The obtained results show that the helix angle has the highest overall importance value, followed by feed per tooth. And the optimum combination of parameters for tool wear rate and material removal rate is obtained respectively.

The operation and maintenance stage of the long-span prestressed steel structure is the core link of the whole life cycle. At present, there are few studies on the change law of safety risk in the whole process of operation and maintenance, especially the research on the analysis and prediction of the change law of safety risk in the whole process of structural operation and maintenance by effectively using the abundant monitoring data and relevant safety risk information in the operation and maintenance stage, which also affects the prestressed steel, which also affects the efficiency of judgment and control decision-making of operation and maintenance safety state of prestressed steel structure. Taking the spoke-type cable truss as an example, this paper proposes a new concept of integrating the digital twin model (DTM) with steel structure operation and maintenance safety. Through the combination of real physical space dimensions and digital virtual space dimensions, it is based on a hypothetical analysis model. In the above, a theoretical framework is proposed, and a case analysis of a prestressed steel structure is carried out from big data, and the feasibility of applying this method in the prestress loss and uneven rain and snow load conditions is evaluated. This method can provide guidance for operation and maintenance management and formulate strategies in time.

Ball-end cutters are widely used in industries of dies, molds, and aerospace, which have the problem of poor machined surface quality due to the low cutting speed near the tool-tip. With the increase in the complexity of parts, it will become more and more difficult to avoid the tool-tip participating in the cutting. In this paper, the velocity effect sensitivity of ball-end cutter is analyzed, and several key positions, including the intersection points of the CWE boundaries, are selected to describe the cutting speed in three dimensions. The relationships between the cutting speed of the critical points and important variables such as: machining inclination angle and the feed direction were investigated. The optimal range of feed direction is obtained when the tool-tip engages in the contact circle. The core aim of the feed direction selection is to make the tool engagement area in a high position by changing the feed direction, to avoid surface damage caused by ploughing and improve the quality of the machined surface. Finally, an experimental study was carried out, and the results corroborate the effectiveness of the selection method. In the experiment, it was also found that cutting-out from the cutter contact position can improve the surface quality in the directions of non-optimal range, and the milling force and chips shape will vary with the change of the feed direction.

In the process of metal cutting, the cutting performance of cutting tools varies with different parameter combinations, so the results of the performance indicators studied are also different. So in order to achieve the best performance indicator it is necessary to get the best parameter matching combination. In addition, in the process of metal cutting, the value of the performance index is different at each stage of the processing process. In order to consider the cutting process more comprehensively, it is necessary to use a comprehensive evaluation method that can evaluate the dynamic process of performance indicators. This paper uses a dynamic evaluation method that considers the dynamic change of performance indicators in each stage of the cutting process to comprehensively evaluate the tool parameters and cutting parameters at each level. For the purpose of high processing efficiency and long tool life, tool wear rate and material removal rate are used as performance indicators. In the case of specified rake angle, cutting speed and cutting width, titanium alloy is studied by end milling cutter side milling. The tool parameters and cutting parameters in milling process are optimized by using a dynamic comprehensive evaluation method based on gain horizontal excitation. Finally, the parameter matching combination that can make the performance indicator reach the best is obtained. The results show that when the rake angle is 8°, the cutting speed is 37.68 m/min, and the cutting width is 0.2 mm, the tool wear rate and material removal rate are the best when the clearance angle is 9°, the helix angle is 30°, the feed per tooth is 0.15 mm/z, and the cutting depth is 2.5 mm.