Search results

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

Building information modelling (BIM) has transformed the traditional practices of the Architecture, Engineering and Construction (AEC) industry. BIM creates a collaborative digital representation of built environment data. Competitive advantage can be achieved with collaborative project delivery and rich information modelling. Despite the abundant benefits, BIM’s adoption in the AEC is susceptible to confrontation. A substantial impediment to BIM adoption often cited is data interoperability. Other facets of interoperability got limited attention. Other academic areas, including information systems, discuss the interoperability construct ahead of data interoperability. These interoperability factors have yet to be surveyed in the AEC industry. This study aims to investigate the effect of interoperability factors on BIM adoption and develop a comprehensive BIM adoption model.

The theoretical foundations of the proposed model are based on the European interoperability framework (EIF) and technology, organization, environment framework (TOE). Quantitative data collection from construction firms is gathered. The model has been thoroughly examined and validated using partial least squares structural equation modelling in SmartPLS software.

The study’s findings indicate that relative advantage, top management support, government support, organizational readiness and regulation support are determinants of BIM adoption. Financial constraints, complexity, lack of technical interoperability, semantic interoperability, organizational interoperability and uncertainty are barriers to BIM adoption. However, compatibility, competitive pressure and legal interoperability do not affect BIM adoption.

Finally, this study provides recommendations containing the essential technological, organizational, environmental and interoperability factors that AEC stakeholders can address to enhance BIM adoption.

To the best of the authors’ knowledge, this paper is one of the first studies to combine TOE and EIF in a single research model. This research provides empirical evidence for using the proposed model as a guide to promoting BIM adoption. As a result, the highlighted determinants can assist organizations in developing and executing successful policies that support BIM adoption in the AEC industry.

The aim of this study was to conduct a comprehensive investigation into declining and emerging occupations and job titles and to develop a national career progression pathway for the tourism and hospitality (T&H) sector.

Anchored on the Social Cognitive Career Theory, this study used face to face in-depth interviews of 33 industry stakeholders: policymakers, trade association, training providers and beneficiaries (T&H).

The finding reveals that only the “watchman” occupation was identified as the declining job while majority of the emerging jobs were more related to information technology and environmental occupations (website designers, digital marketers, data analysts, hygienists, and safety and hazard experts).

The findings provide a valuable signal for the growing number of jobs in security services, hygiene and information technology-oriented occupations, which the Ministry of Tourism, Arts and Culture including practitioners including HR directors and general managers should respond timely to and to these growing needs in order to remain competitive in the sector.

This is the first study in context that responded to a call by industry players to fill in a practical knowledge gap in examining declining and emerging jobs and job titles in the T&H sector. The study provides vocational insights into mapping the entry level requirements for the jobs allied with occupations in the national technical and vocational educational training qualifications framework of Ghana at the national level.

Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.

To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.

The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.

The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

Because of the advantages of high deposition efficiency and low manufacturing cost compared with other additive technologies, robotic wire arc additive manufacturing (WAAM) technology has been widely applied for fabricating medium- to large-scale metallic components. The additive manufacturing (AM) method is a relatively complex process, which involves the workpiece modeling, conversion of the model file, slicing, path planning and so on. Then the structure is formed by the accumulated weld bead. However, the poor forming accuracy of WAAM usually leads to severe dimensional deviation between the as-built and the predesigned structures. This paper aims to propose a visual sensing technology and deep learning–assisted WAAM method for fabricating metallic structure, to simplify the complex WAAM process and improve the forming accuracy.

Instead of slicing of the workpiece modeling and generating all the welding torch paths in advance of the fabricating process, this method is carried out by adding the feature point regression branch into the Yolov5 algorithm, to detect the feature point from the images of the as-built structure. The coordinates of the feature points of each deposition layer can be calculated automatically. Then the welding torch trajectory for the next deposition layer is generated based on the position of feature point.

The mean average precision score of modified YOLOv5 detector is 99.5%. Two types of overhanging structures have been fabricated by the proposed method. The center contour error between the actual and theoretical is 0.56 and 0.27 mm in width direction, and 0.43 and 0.23 mm in height direction, respectively.

The fabrication of circular overhanging structures without using the complicate slicing strategy, turning table or other extra support verified the possibility of the robotic WAAM system with deep learning technology.

This article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.

Robots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

Ad hoc mobile networks are commonplace in every aspect of our everyday life. They become essential in many industries and have uses in logistics, science and the military. However, because they operate mostly in open spaces, they are exposed to a variety of dangers. The purpose of this study is to introduce a novel method for detecting the MAC layer misbehavior.

The proposed novel approach is based on exponential smoothing for throughput prediction to address this MAC layer misbehavior. The real and expected throughput are processed using an exponential smoothing algorithm to identify this attack, and if these metrics exhibit a trending pattern, an alarm is then sent.

The effect of the IEEE 802.11 MAC layer misbehavior on throughput was examined using the NS-2 network simulator, as well as the approval of our novel strategy. The authors have found that a smoothing factor value that is near to 0 provides a very accurate throughput forecast that takes into consideration the recent history of the updated values of the real value. As for the smoothing factor values that are near to 1, they are used to identify MAC layer misbehavior.

According to the authors’ modest knowledge, this new scheme has not been proposed in the state of the art for the detection of greedy behavior in mobile ad hoc networks.