Impact of enhanced context-awareness for construction field crews on task cycle time

Lacking awareness of context in a timely manner has hampered field communication on construction jobsites. As a result, field crews are often not informed of the right information at the right time and the performance of field tasks may not be as good as necessary. This thesis provides a context taxonomy consisting of 17 context types and context-based work instructions that improve field communication by enhancing context-awareness. In addition, a case study shows that context-based work instructions improve field task performance by reducing the variability of cycle time. Two research questions drove this research. The first question is (1) How to define context and develop a context taxonomy for construction field crews so that they could be informed of context information and its changes? A field study of 40 instances of field task problems on four construction jobsites showed that field crews were not sufficiently informed of context information and its changes. For example, a precast shear wall crew did not find out that the supporting brace was not of the right size until a shear wall was lifted to the installation location. Another example showed that a crew was notified to double the number of internet cables after the ceiling was installed. To address this question, the overall strategy was to clearly understand context for field crews and then I decided to explore context modeling methods to formally represent field context in a machine-interpretable way and support reasoning of context. First, therefore, I put forward an adjusted definition of field context based on a synthesis of previous efforts to define context. Second, a context taxonomy, consisting of 17 specific context types, was developed to provide a common vocabulary for describing field context. The 17 context types were validated through semi-structured interviews with 15 experienced industry professionals from sophisticated construction companies. Third, I built an ontology-based construction context information (CCI) model based on the validated context types. The CCI model defines relationships between these context types and their attributes. This research shows that, with the model, it is feasible to define reasoning rules to derive high-level context (e.g., a task's status) from low-level context (i.e., context that can be directly defined or sensed). A case study on a precast parking structure project indicates that the cycle time of construction field tasks varies significantly due to crews' unawareness of context and its changes. I then wondered whether the cycle time of field tasks would be affected and, if so, how if field crews were provided with instructions incorporating field context information based on the CCI model. Hence, I asked the second research question: (2) What is the impact of context-based work instructions on the cycle time of construction field tasks? To answer this question, the overall strategy was to create context-based work instructions and design a controlled experiment to compare my proposed instructions with state-of-the-art instructions in terms of the impact on task cycle time. First, based on a review of features of high-quality work instructions, I decided to focus on two distinguishing features: informed of changes and just-in-time. By meeting these two distinguishing features, the context-based work instructions formalized in this research can help inform field crews of context information and its changes in a timely manner. Second, I created context-based work instructions, the implementation of which was designed to align with short cycle planning processes through a mobile-based prototype due to the need to communicate context information in a just-in-time way. To evaluate how context-based work instructions affect field tasks, I conducted a controlled experiment on an office renovation project. The controlled experiment was performed over 14 groups of field tasks involving 246 instances of field tasks. Each group was equally divided into a control sub-group and a treatment sub-group. For the control sub-group, field crews were provided with state-of-the-art work instructions, while for the treatment sub-group, context-based work instructions were implemented. For each instance of a field task, a percent deviation was calculated by dividing the delta between the actual and the planned cycle time by the planned cycle time. A small percent deviation indicates that the planned cycle time is close to the actual, suggesting that the plan reflected the actual situation. The calculations showed that, across the 14 groups of tasks, the average percent deviation of the treatment sub-groups was 35.7% less than that of the control sub-groups. Based on the calculations, I find context-based work instructions help reduce the variability of the cycle time of field tasks with respect to planned vs. actual cycle times. The case study results also show that the actual cycle time varies less from day to day for the treatment group vs. the control group. Together, these findings imply that context-based work instructions help avoid overruns of task cycle time. Overall, this thesis contributes a context taxonomy consisting of 17 context types as a common vocabulary for describing field context and context-based work instructions that communicate context information in a just-in-time way and help inform field crews of changes in context. The case study showed that, with context-based work instructions, the performance of field tasks was enhanced in that the variability in the cycle time of field tasks was reduced. Future work should study the generality of the results by evaluating the formalized work instructions in other projects and field tasks

Advisor: Fischer Martin
Khatib Oussama
Lepech Michael
Schwegler Benedict R.