ORCID Profile
0000-0001-8456-3342
Current Organisation
London South Bank University
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Publisher: Elsevier BV
Date: 12-2021
Publisher: MDPI AG
Date: 24-11-2021
DOI: 10.3390/BUILDINGS11120577
Abstract: With the rapid development of new technologies, such as big data, the Internet of Things (IoT) and intelligent sensing, the traditional asphalt pavement construction quality evaluation method has been unable to meet the needs of road digital construction. At the same time, the development of such technologies enables a new management system for asphalt pavement construction. In this study, firstly, the dynamic quality monitoring system of asphalt concrete pavement is established by adopting the BeiDou Navigation Satellite System, intelligent sensing, the IoT and 5G technology. This allows key technical indicators to be collected and transmitted for the whole process of asphalt mixture, which includes the mixing plant, transport vehicle, paving and compaction. Secondly, combined with AHP and the entropy weight (EW) method, the index combination weight is calculated. The comprehensive index for the pavement digital construction quality index (PCQ) is proposed to reflect the impact of monitoring indicators on pavement quality. An expert decision-making model is formed by using the improved particle swarm optimization (PSO) algorithm coupled with radial basis function neural network (RBF). Finally, the digital monitoring index and pavement performance index are connected to establish a full-time and multi-dimensional digital construction quality evaluation model. This study is verified by a database created from the digital monitoring data of pavement construction collected from a highway construction project. The system proposed in this study can accurately reflect the quality of pavement digital construction and solve the lag problem existing in the feedback of construction site.
Publisher: Thomas Telford Ltd.
Date: 04-2021
Abstract: There are few studies analysing whether different types of environmental regulation have differential impacts on the efficiency of the construction industry. Using 2012–2016 panel data from 30 provinces in China, the green total factor productivity (GTFP) of the construction industry is measured with a global Malmquist–Luenberger productivity index based on the epsilon measure model. Thereafter, a panel tobit regression model is proposed to explore the relationship between three types of environmental regulation and the GTFP of the construction industry. The results show that (a) from 2012 to 2016, the GTFP of the Chinese construction industry grew slowly at an average annual rate of 0.14% (b) both one-phase lagged command-and-control and current phase market-based environmental regulation had a positive linear relationship with GTFP, while one-phase lagged voluntary environmental regulation, on the other hand, had an inverted U-shaped relationship with GTFP (c) the three types of environmental regulation can be combined to establish a suitable environmental regulation system. The findings of this study provide guidance for the sustainable development of the construction industry by combining the actions of different types of environmental regulation.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 02-2021
Publisher: Wiley
Date: 11-08-2021
DOI: 10.1002/CAE.22448
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Simon Patrick Philbin.