An integrated data collection and analysis framework for remote monitoring and planning of construction operations

Abstract

Recent advances in data collection and operations analysis techniques have facilitated the process of designing, analyzing, planning, and controlling of engineering processes. Mathematical tools such as graphical models, scheduling techniques, operations research, and simulation have enabled engineers to create models that represent activities, resources, and the environment under which a project is taking place. Traditionally, most simulation paradigms use static or historical data to create computer interpretable representations of real engineering systems. The suitability of this approach for modeling construction operations, however, has always been a challenge since most construction projects are unique in nature as every project is different in design, specifications, methods, and standards. Due to the dynamic nature and complexity of most construction operations, there is a significant need for a methodology that combines the capabilities of traditional modeling of engineering systems and real time field data collection. This paper presents the requirements and applicability of a data-driven modeling framework capable of collecting and manipulating real time field data from construction equipment, creating dynamic 3D visualizations of ongoing engineering activities, and updating the contents of a discrete event simulation model representing the real engineering system. The developed framework can be adopted for use by project decision-makers for short-term project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities.

Introduction

Resource planning and control at the operations level are critical components of managing the performance of ongoing activities in a construction site [1]. A comprehensive operations level plan can help project decision-makers and site personnel foresee potential problems such as spatial conflicts and resource under utilization before the actual operation takes place. This will also help save effort that would have otherwise been put on reworks, resolving conflicts, and performing change orders, and will ultimately translate into significant savings in project time and cost. For example, Cox et al. [2] suggested that rework is typically responsible for 6–12% of the overall expenditure for a construction project. Construction Industry Dispute Avoidance and Resolution Task Force (DART) reported that annually more than $60 billion was spent on change orders in the United States [3]. Also, according to the Federal Facilities Council (FFC), in 10–30% of all construction projects serious disputes are estimated to arise with a total cost of resolution between $4 and $12 billion each year [4]. One of the major impediments of effective planning is managing a large volume of information including inputs from alternative project designs, material properties, labor productivity, equipment specifications, and project schedules. This will become even more sophisticated when the dynamics of the construction project introduces several layers of uncertainty that can range from internal factors (e.g. project time and cost variations, equipment breakdowns, contractor claims) to external events (e.g. weather conditions, financial market stability). Computer applications have thus evolved during the past several years to facilitate the process of project planning by providing a convenient and reliable means for modeling, simulating, and visualizing project activities [5], [6], [7], [8], [9], [10], [11]. In order to create reliable computer models of a future construction project during the planning stage, one needs to carefully examine every detail of the operations within that project, and identify major events and processes that will potentially impact the outcome of an operation. Once such events and processes are identified, attributes such as resource consumption levels and activity durations should be determined. For a small operation, this can be done in a relatively short period of time using existing numerical tools and statistical data from past projects. However, as the size of the operation increases and with the introduction of more resources and activities, creating a simulation model that realistically represents the actual operation becomes a tedious if not an impossible task. This is mainly due to the fact that collecting accurate and reliable field data from ongoing activities and resource operations, and integrating the collected data into the planning process turns into a challenging job. In addition, unforeseen site conditions, equipment breakdowns, work delays, and the evolving nature of a construction project will introduce additional layers of uncertainty that may slow down the progress of data collection. Even if all such data is collected, handling a large volume of information in a single platform may prove to be a time and labor intensive task. As a result, it is very likely that the modeler uses strict rules, simplifying assumptions, and rigid design parameters inside the model. These may seriously impact the accuracy of the model in representing the dynamics of the project which will ultimately be detrimental to the reliability of the model for verification and validation [12].