Search for: All records

Physics-based simulations are essential for designing autonomous construction equipment, but preparing models is time-consuming, requiring the integration of mechanical and geometric data. Current automatic modeling methods for modular robots are inadequate for construction equipment. This paper explores automating the modeling process by integrating mechanical data into 3D computer-aided design (CAD) models. A template library is developed with hierarchy and joint templates specific for equipment. During model generation, appropriate templates are selected based on the equipment type. Unspecified joint template data is extracted from technical specifications using a large language model (LLM). The 3D CAD model is then converted into a Universal Scene Description (USD) model. Users can adjust the part names and hierarchy within the USD model to align with the hierarchy template, and joint data is automatically integrated, resulting in a simulation-ready model. This method reduces modeling time by over 87 % compared to manual methods, while maintaining accuracy. 

The integration of deep learning (DL) into construction applications holds substantial potential for enhancing construction automation and intelligence. However, successful implementation of DL necessitates the acquisition of substantial data for training. The acquisition process can be error-prone, time-consuming, and impractical. For this reason, synthetic simulated data (SSD) has emerged as a promising alternative. While various strategies have been developed to generate such data, a systematic review and evaluation are lacking to aid researchers and professionals in selecting appropriate strategies for their applications. To fill this gap, this paper conducts a comprehensive literature review related to SSD generation and applications, and develops a guideline for strategy selection. Two hundred and eight articles are identified from the academic database Web of Science by using PRISMA. After thoroughly analyzing the literature, seven SSD generation strategies are identified and evaluated across six metrics. Based on the performance of each strategy, a guideline is synthesized as a decision tree. Users only need to follow the steps and answer the questions in the decision tree, and then they will get the recommended SSD generation strategy. We demonstrate the guideline’s effectiveness by comparing its recommendations with the strategies chosen by researchers in existing DL construction applications and achieve a matching rate of 82%. 

The labor-intensive nature of the construction industry requires workers to frequently perform physically demanding manual work, thereby exposing them to the risk of musculoskeletal injury (approximately 31.2 cases per 10,000 full-time equivalent workers). Exoskeletons and exosuits (collectively called EXOs here) are designed to protect workers from these injuries by reducing exertion and muscle fatigue during work. However, the usability of EXOs in construction is still not clear. This is because extant EXO assessments in construction were mainly conducted in laboratory environments with test participants who are not construction professionals. In this research, we conducted a pilot study to investigate the usability of EXOs in a real construction workplace. Four experienced workers were recruited to push/empty construction gondolas with and without a Back-Support EXO, HeroWear Apex. Three workers were recruited to install/remove wooden blocks between steel studs with and without two Arm-Support EXOs, i.e., Ekso EVO and Hilti EXO-001. Their motions, postures, heart rates, and task completion times were recorded and compared. The workers were also surveyed to gather their attitudes toward the EXO’s usefulness and ease of use. The study results demonstrated that the workers responded to the use of EXOs differently and consequently were not unanimously in favor of EXO adoption in practice. The preliminary results and findings from this pilot study help in building a foundation of understanding to improve EXO products to fit the needs of construction workers and foster EXO-enabled construction tasks in the future. 

Exoskeletons and exosuits (collectively termed EXOs) have the potential to reduce the risk of work-related musculoskeletal disorders (WMSDs) by protecting workers from exertion and muscle fatigue due to physically demanding, repetitive, and prolonged work in construction workplaces. However, the use of EXOs in construction is in its infancy, and much of the knowledge required to drive the acceptance, adoption, and application of this technology is still lacking. The objective of this research is to identify the facilitators, barriers, and corresponding solutions to foster the adoption of EXOs in construction workplaces through a sequential, multistage Delphi approach. Eighteen experts from academia, industry, and government gathered in a workshop to provide insights and exchange opinions regarding facilitators, barriers, and potential solutions from a holistic perspective with respect to business, technology, organization, policy/regulation, ergonomics/safety, and end users (construction-trade professionals). Consensus was reached regarding all these perspectives, including top barriers and potential solution strategies. The outcomes of this study will help the community gain a comprehensive understanding of the potential for EXO use in the construction industry, which may enable the development of a viable roadmap for the evolution of EXO technology and the future of EXO-enabled workers and work in construction workplaces.