Abstract
Annual productivity growth within Construction has increased by just 1% in over 20 years (McKinsey, 2017). A key contributor to this performance issue is associated with the management of construction’s equipment fleet. Site equipment represent a significant percentage of the total cost of projects; are a critical resource linked to delays, and a major contributor to on- and off- site congestion and air pollution. Hence, their effective management represent a key opportunity for economic, environmental and safety efficiency gains.
Research and development, in both industry and academia, to address this challenge is on the rise in particular with efforts focussing on exploiting opportunities from the Internet of Things (IoT). Studies are addressing accidents prevention, measurement of operational efficiency, monitoring equipment health, and improving equipment deployment. Equipment default telematics systems are increasingly providing important operational data (e.g., working hours, fuel consumption, fault code, etc.) but these have many limitations such as: they are restricted to heavy equipment; they do not capture all data required for effective management of construction equipment fleet, and data is not live and not aggregated from across disparate systems to enable intelligent decisions. Moreover, data from IoT systems is not linked to any Building Information Modelling (BIM) environment (e.g. 4D BIM), making it challenging to contextualise the operation of construction fleet equipment and to inform their deployment decisions.
This paper proposes an integrated IoT-BIM system for the management of site equipment. Eight interviews were held with experts from the largest infrastructure projects in the UK to identify the key challenges facing site equipment fleet and the key use cases to be addressed. A review of existing telematics systems was performed to identify their capabilities and limitations in relation to an integrated IoT-BIM solution for the identified use cases. An early architecture of the proposed IoT-BIM system is described. The proposed system requires i) the collection of data from disparate telematics systems and the development of tailored IoT devices for data generation and collection, ii) machine learning algorithms to interpret the data for the use cases’ decisions, and iii) data and information from both the IoT and the machine learning unit into the 4D BIM environment and the dashboard visualising the performance for the selected use cases.
This work is part of a joint industry-academic effort co-financed by Innovate UK as a feasibility study project under their ‘Increase Productivity, Performance and Quality in UK Construction’ competition.
Research and development, in both industry and academia, to address this challenge is on the rise in particular with efforts focussing on exploiting opportunities from the Internet of Things (IoT). Studies are addressing accidents prevention, measurement of operational efficiency, monitoring equipment health, and improving equipment deployment. Equipment default telematics systems are increasingly providing important operational data (e.g., working hours, fuel consumption, fault code, etc.) but these have many limitations such as: they are restricted to heavy equipment; they do not capture all data required for effective management of construction equipment fleet, and data is not live and not aggregated from across disparate systems to enable intelligent decisions. Moreover, data from IoT systems is not linked to any Building Information Modelling (BIM) environment (e.g. 4D BIM), making it challenging to contextualise the operation of construction fleet equipment and to inform their deployment decisions.
This paper proposes an integrated IoT-BIM system for the management of site equipment. Eight interviews were held with experts from the largest infrastructure projects in the UK to identify the key challenges facing site equipment fleet and the key use cases to be addressed. A review of existing telematics systems was performed to identify their capabilities and limitations in relation to an integrated IoT-BIM solution for the identified use cases. An early architecture of the proposed IoT-BIM system is described. The proposed system requires i) the collection of data from disparate telematics systems and the development of tailored IoT devices for data generation and collection, ii) machine learning algorithms to interpret the data for the use cases’ decisions, and iii) data and information from both the IoT and the machine learning unit into the 4D BIM environment and the dashboard visualising the performance for the selected use cases.
This work is part of a joint industry-academic effort co-financed by Innovate UK as a feasibility study project under their ‘Increase Productivity, Performance and Quality in UK Construction’ competition.
| Original language | English |
|---|---|
| Pages | 900 |
| Number of pages | 910 |
| Publication status | Published - 18 Sept 2019 |
| Event | 36th CIB W78 2019 Conference: Advances in ICT in Design, Construction and Management in Architecture, Engineering, Construction and Operations (AECO) - Northumbria University, Newcastle upon Tyne, United Kingdom Duration: 18 Sept 2019 → 20 Sept 2019 http://cibw78.northumbria-eee.co.uk/ |
Conference
| Conference | 36th CIB W78 2019 Conference |
|---|---|
| Abbreviated title | CIBW78 |
| Country/Territory | United Kingdom |
| City | Newcastle upon Tyne |
| Period | 18/09/19 → 20/09/19 |
| Internet address |