Abstract

Road construction is fundamentally different to building construction, in terms of its products, the types of work and operations, and the resources used. One of the key differences from the point of view of planning and control of production flow is that roads are composed of geometrically continuous courses rather than discrete ‘products’, making work packaging difficult. Clearly defined work packages that comprise work performed on distinct products are central to lean construction methods of planning and control, such as the Last Planner System. We therefore propose a product schema which models road sections with distinct road course segments that are dynamically defined aggregations of 'roadels'. A roadel is a fine-grained vertical triangular prism object that can be dynamically aggregated with other roadels to represent a road course segment associated with a work package, a planned task, or an as-built section. The schema represents the continuous nature of road construction, and its discrete entities enable representation and computations of as-made work using the raw data obtained from machine-mounted sensors and land surveys. We describe the information schema and illustrate its use for computation and analysis of lean production flow metrics. The schema has been tested using case study data from a 25,000 m2 parking lot project, which was modeled with more than 560,000 roadels with as-made status data collected from 33 working days over a period of two months.

Highlights

  • Many Building Information Modelling (BIM) tools have native schema that represent buildings and building objects in ways that support design, analysis and detailing, and project management (Sacks et al 2018)

  • The second question is how to use the data acquired to compute meaningful metrics of process flow. Existing metrics, such as the Percent Plan Complete (PPC) metric of the Last Planner System® (LPS) (Ballard 2000) or the Construction Flow Index (CFI) (Sacks et al 2017), are calculated using the production data associated with fine-grained definitions of tasks, which themselves are derived from work packages that comprise specific building model objects

  • For road construction to benefit from new production control tools such as the LPS and from new technological innovations, the continuous and dynamic nature of the products, the ways in which they can be divided into sub-products and aggregated, and how they can be associated with fine-grained production planning and control tasks, must be reconsidered

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Summary

INTRODUCTION

Many Building Information Modelling (BIM) tools have native schema that represent buildings and building objects in ways that support design, analysis and detailing, and project management (Sacks et al 2018). Like most BIM technologies and tools, the IFC information schema (ISO 2013) was developed originally for building construction projects, with hierarchical and well defined products (elements and spaces), and its schema contains the entities and relationships needed for exchange of production planning and control information. Existing BIM tools for design and construction of roads do not provide the level of detail and the relationships needed in their native schema for effective production control of earthworks. We propose a product schema for lean construction production planning and control of the earthworks and surfacing operations of road construction projects. The paper concludes with discussion of the considerations for binding the proposed schema to the IFC standard, of the earned-value and the production flow metrics, and of the limitations of the schema

Production control for road construction
Information Schema for Infrastructure
Summary
USE CASES
PROPOSED INFORMATION SCHEMA
PRODUCTION DATA ANALYSIS
Metrics of planning reliability
Continuous Planned Percentage Complete
Planning Effectiveness Index
Illustration of CPPC and PEI values
Metrics of production flow
DISCUSSION
Considerations for information exchange
Linear and discrete project objects
Development of Continuous Product Flow Metrics
Support for autonomous production planning
CONCLUSION

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