A hierarchical machine learning framework for the identification of automated construction
نویسندگان
چکیده
A robust monitoring system is essential for ensuring safety and reliability in automated construction. Activity recognition one of the critical tasks monitoring. Existing studies this area have not fully exploited potential enhancing performance machine learning algorithms using domain knowledge, especially problem formulation. This paper presents a hierarchical framework improving accuracy identification Automated Construction System (ACS) operations. The proposed arranges operations to be identified form hierarchy uses multiple classifiers that are organized hierarchically separating operation classes. It tested on laboratory prototype an ACS, which follows top-down construction method. ACS consists set lightweight portable machinery designed automate structural frame low-rise buildings . Accelerometers were deployed at locations structure. acceleration data collected while operating equipment used identify through techniques. compared with conventional approach involves flat list classes separated. was comparable top level. However, outperformed when fine levels identified. versatility noise tolerance also reported. Results demonstrate robust, it feasible precisely. Although validated full-scale effects strong ambient disturbances actual sites been evaluated. study will support development assist main operator ensure safe high-level details purpose can utilised project assessment progress application outlined.
منابع مشابه
a study on construction of iranian life tables: the case study of modified brass logit system
چکیده ندارد.
15 صفحه اولapsis - Framework for Automated Optimization of Machine Learning Hyper Parameters
Machine learning and the algorithms used for it have become more and more complex in the past years. Especially the growth of Deep Learning architectures has resulted in a large number of hyperparameters such as the number of hidden layers or the transfer function in a neural network which have to be tuned to achieve the best possible performance. Since the result of a hyperparameter choice can...
متن کاملMachine Learning Identification of Zeolite Framework Types
The characteristic framework types of zeolite crystals are routinely determined by calculating coordination sequences and vertex symbols of the 3D crystal structures. This method has limitations and tends to fail when the synthesized crystals are not close to perfect and present some types of crystallographic disorder. A machine learning based Zeolite-Structure-Predictor (ZSP) model is develope...
متن کاملEvaluating Machine Learning Algorithms for Automated Network Application Identification
The identification of network applications that create traffic flows is vital to the areas of network management and surveillance. Current popular methods such as port number and payload-based identification are inadequate and exhibit a number of shortfalls. A potential solution is the use of machine learning techniques to identify network applications based on payload independent statistical f...
متن کاملinvestigating the feasibility of a proposed model for geometric design of deployable arch structures
deployable scissor type structures are composed of the so-called scissor-like elements (sles), which are connected to each other at an intermediate point through a pivotal connection and allow them to be folded into a compact bundle for storage or transport. several sles are connected to each other in order to form units with regular polygonal plan views. the sides and radii of the polygons are...
ذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Information Technology in Construction
سال: 2021
ISSN: ['1874-4753']
DOI: https://doi.org/10.36680/j.itcon.2021.031