Automation in Construction最新文献

This paper introduces a numerical framework to model the 3D concrete printing process, considering critical factors, particularly the print path and interlayer interactions. Within this framework, a finite element model is automatically generated for an arbitrary 3D-printed object. This is achieved by voxelizing the bounding space, incorporating a zero-thickness interlayer cohesive zone, and pinpointing the active elements. Additionally, a print path-driven element segment algorithm is developed, allowing for sequential element placement in alignment with the print path during simulation, thereby mirroring the actual printing process. The model efficacy is demonstrated through two benchmarks, focusing on elastic buckling and plastic failure, where it agrees with existing experimental and numerical data. Using this validated model, the impacts of various printing parameters, such as print width, speed, path, and interlayer behaviors are explored, and an integrated toolbox for both educational and academic purposes is created. This toolbox is available at https://github.com/Baixi-Chen/3DCPProcessSimulaion.git.

Robots have the potential to enhance safety on construction job sites by assuming hazardous tasks. While existing safety research on physical human-robot interaction (pHRI) primarily addresses collision risks, ensuring inherently safe collaborative workflows is equally important. For example, ergonomic optimization in co-manipulation is an important safety consideration in pHRI. While frameworks such as Rapid Entire Body Assessment (REBA) have been an industry standard for these interventions, their lack of a rigorous mathematical structure poses challenges for using them with optimization algorithms. Previous works have tackled this gap by developing approximations or statistical approaches that are error-prone or data-dependent. This paper presents a framework using Reinforcement Learning for precise ergonomic optimization that generalizes to different types of tasks. To ensure practicality and safe experimentations, the training leverages Inverse Kinematics in virtual reality to simulate human movement mechanics. Results of a comparison between the developed framework and ergonomically naive approaches are presented.

The intelligent detection of pavement distress using deep learning methods has consistently been a hotspot in pavement maintenance. This paper aims to offer new insights to promote research and application in this field through bibliometric analysis. Utilizing publications from the Web of Science Core Collection spanning from 2016 to 2024 as the database, this paper conducts a systematic analysis of statistical data concerning the annual publication numbers, countries/regions, institutions, authors, hot papers, disciplines, and journals. Based on deep learning models, datasets, and the state of practice, this analysis explores the hotspots and fronts of this field. It identifies gaps, challenges, and future research directions, including the exploration and optimization of models, the quality and variability of datasets, the evolution of data acquisition methods, the impact of the state of practice, the prospects of unmanned detection technologies, the integration of multi-source heterogeneous data, and the potential of digital twin technologies.

In the subcontractor (Sub) procurement process, the General Contractor (GC) can seek potential Subs from a limited pool based on their past relationships. This challenges new subcontractors who are qualified for the project but lack prior relationships with the GC. Furthermore, it creates a relationship bias that impedes the creation of a constructive business environment where potential Subs are encouraged to enhance their quality and compete fairly to secure procurement contracts. To address these challenges, this paper proposes a proof-of-concept of a transformational procurement system that integrates blockchain-enabled smart contracts and Building Information Modeling (BIM)-based project management. The proposed system can facilitate the data-driven automatic matching of GC with Subs, thus extending the pool of Subs and eliminating relationship bias in the procurement proceedings to enhance the effectiveness and fairness of the procurement. It contributes to the body of knowledge by enabling a digitalized and automated subcontractor procurement leveraging blockchain.

Extrusion based 3D Concrete Printing (E-3DCP) is a rapidly growing method of construction due to its ability to manufacture bespoke architectural and structural elements without incurring the additional time and costs typically associated with the manufacturing of the formwork of these components. However, Complex geometries such as overhangs, bridges, and cantilevers pose significant challenges to E-3DCP, increasing the risk of premature failure during 3D printing and complicating the integration of longitudinal reinforcement. In response to these challenges, researchers have developed various techniques and strategies to mitigate these complexities and failures. This paper provides a comprehensive review of these methods, evaluating their advantages and disadvantages, and identifying research gaps in the current literature.