Statistical examination of robotics implementation in India’s construction industry
Srinivasa Rao Allu, Amiya Bhumik, Shaik Chandini
- Year
- 2025
- Citations
- 1
- Access
- Open access
Abstract
The Indian Construction Sector (CS) plays a crucial role in the nation’s economy, but continues to grapple with challenges such as low productivity, labor inefficiencies, and safety concerns. Robotics and automation offer transformative solutions to address these issues. This study statistically explores the current status, awareness, willingness, and perceived performance of robotics in the construction sector, focusing on Andhra Pradesh, India. A survey-based approach was adopted, collecting data under a snowball sampling technique from 196 construction workers, contractors, and consultants through a structured questionnaire. Linear Regression Analysis (LRA) explores the hypothesis testing, analysing the relationships between robotics adoption and factors such as awareness, willingness, and project delivery outcomes. Results indicate moderate awareness of robotics technologies; however, the willingness to adopt remains low due to barriers like high costs, lack of skilled operators, and resistance to change. Despite this, respondents acknowledged the positive effects of robotics on improving project quality, efficiency, and safety. While mixed perceptions were observed regarding expected performance, 94.7% of respondents emphasised the need to promote robotics adoption through government support, training programs, and awareness campaigns. The study concludes that addressing barriers related to cost, skill gaps, and cultural resistance is essential for enabling the widespread adoption of robotics, thereby enhancing productivity, safety, and sustainability in the Indian construction sector.
Keywords
Related papers
Artificial intelligence: a modern approach
1995
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
2012
Self-Organizing Maps
Teuvo Kohonen
1995
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham +17 more
2016