Asad Udaipurwala

Computer system for the selection of trenchless and conventional methods for underground utilities

Abstract This paper describes the current status of an ongoing research project, the goal of which is to develop a knowledge-based methods-selection tool that can assess specific construction conditionsagainst the capabilities and limitations off available techniques to determine the best method(s) for a specific project. A flexible system architecture is presented which consists of a Standards Level, a Project Level, and an interface between the two. The Standards Level facilitates the capture of experience, expertise and market information about current methods and supporting resources as well as standard descriptions of project elements. The Project Level provides a description of the physical elements and the site context for a given project along with the methods selected for the various steps in the construction process. The interface permits the easy use of coded knowledge, and allows the user discretion in the level of assistance the system should offer in selecting construction methods and formulating an overall game plan. The system architecture builds on a specific vocabulary and supporting constructs for describing methods. Elements of this vocabulary include Method Statement, Operation, Method Class, Method, Resource Class and Resource. The installation of underground utilities using both trenchless and conventional technologies provides a context for the work, and is used in the paper to illustrate the concepts developed.

HIERARCHICAL CLUSTERING FOR INTERPRETATION OF SPATIAL CONFIGURATION

Ever since the introduction of computers – particularly the field of Artificial Intelligence – construction researchers have endeavored to develop systems that capture and encode the knowledge of seasoned construction practitioners with the goal of at least partially automating tasks such as construction methods selection, equipment selection, constructability reasoning and cost estimation. However, efforts to address these tasks to date have suffered from the lack of a mechanism for automatically inferring conditions such as uniformity/similarity in a facility’s spatial configuration. Identifying these conditions is crucial for evaluating the suitability of a construction method as they affect criteria such as reuse and achievable production rates. In the absence of such a mechanism researchers have relied on either statistical techniques that can be biased by outliers, or simply put the onus on the user by querying them about the number of reuses, etc. which undermines the usefulness of the system itself. In this paper, we introduce an algorithmic technique based on hierarchical clustering that can be used to infer the similarity of part or all of a construction facility with respect to any measure of interest – such as length, area, volume, and so on. The advantage of this technique is that it is immune to outliers in the data set, and it can accommodate the intuitive notion of acceptable variability. For example, in an expert’s judgment, a six percent variability in the dimensions of slab-bays is acceptable for use of flying truss formwork as it can be accommodated with infill panels or hinge panels. We start by providing a motivating example from the domain of building construction, and illustrate how the techniques adopted by researchers to date fail in the case of various spatial configurations. We then provide the hierarchical clustering algorithm in detail after a short discussion of our technique for representing the project’s physical context. Finally, we illustrate how the algorithm has been integrated with a project management system that provides a hierarchical representation of the physical view of a facility, and a production rule based expert system to aid in the selection of construction methods.

Visual Representation of Project Planning and Control Data

In this paper, various visual representations of construction project data are presented, with the intent being to improve decision making in the planning and scheduling phase of a project and analysis of performance to date during the execution and control phase of a project. The value of a multi-view description (process, physical and as-built) of a project in generating these representations is highlighted. Multistory building construction is used to illustrate the concepts presented.

Construction Methods Feasibility Reasoning in an Integrated Environment

The construction technology landscape is changing at a pace far outstripping the capacities of a single decision-maker to stay current and take advantage of new and more efficient innovations. At the same time, projects are becoming more diverse and complex, underscoring the need to exploit such innovations. Also, construction companies are becoming bigger and geographically distributed. There exists a need to develop an IT infrastructure to help in the capture and transfer of hard-earned lessons on previous projects across the entire enterprise. In this paper, we present an architecture for such a system. It permits construction users to bank knowledge gained from industry sources and previous projects, and readily reuse these in formulating construction strategies for new projects. Emphasis is placed on how a number of modular data sources specifying the product and process aspects of construction projects can be loosely-coupled to allow reasoning about construction methods and the generation of an initial schedule.