Impact of autonomous solutions on earthmoving electrification using machine learning: case study
Construction Innovation
; 23(3):606-621, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2290484
ABSTRACT
PurposeThis research aims to investigate the adoption of future technologies in earthmoving applications. The increased development in automated driving systems (ADS) has opened up significant opportunities to revolutionize mobility and to set the path for technologies, such as electrification. The research also aims to explore the impact of automation on electromobility in earthmoving applications.Design/methodology/approachThis paper adopts a multi-objective simulation-based optimization approach using machine learning in earthmoving applications.FindingsThis study concludes that ADS is "conditionally” an enabler for electrification. The study highlights and explains how local and global factors affect this conclusion. In addition to that, the research explores the impact of the equipment size on the integration of future mobility technologies. The shift from "elephant to ants” in the fleet selection resulted in improved feasibility from the integration of ADS in electrification.Originality/valueThis research provides fundamental considerations in the assessment of the impact of autonomous driving solutions on electromobility in the construction industry.
Building And Construction; Earthmoving; Emerging technologies; Simulation optimization; Machine learning; Autonomous vehicle; User experience; Emissions; Electric vehicles; Productivity; Value chain; Automobiles; Air pollution; Carbon footprint; Multiple objective analysis; Automation; Energy consumption; Electrification; Internet of Things; COVID-19; Heavy construction; Artificial intelligence; Costs; Earthmoving equipment; Mining industry; Machinery; Construction industry; Optimization; Construction equipment; Coronaviruses; Disease transmission
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Case report
/
Experimental Studies
Language:
English
Journal:
Construction Innovation
Year:
2023
Document Type:
Article
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