A previously completed study in the field of concrete block construction by Ahmed and Feldman (2012) indicated that, on average, the reinforcing bars in non-contact lap splices, where the lapped bars are located in adjacent cells, only develop 71% of the tensile resistance of spliced bars which are in contact. An experimental program was therefore initiated to design and evaluate remedial measures which can potentially increase the tensile resistance of non-contact lap splices to that of contact lap splice of the same lap length. Implementation of the proposed measures in various field situations was also analyzed. Six unique remedial splice details, along with standard contact and unaltered non-contact lap splices were evaluated and compared. The mitigative details included providing additional confinement, installing knock-out webs, placing splice reinforcement between the lapped bars, and combinations of these aforementioned details. Three replicates of each splice detail were constructed for a total of 24 wall splice specimens. Each wall splice specimen was reinforced with No. 15 Grade 400 deformed steel reinforcing bars with 200 mm lap splice lengths at located the midspan. The specimens were tested in a horizontal position under a monotonic, four-point loading geometry. Load and deflection data were collected throughout testing and were subsequently used in an iterative moment-curvature analysis to calculate the maximum tensile resistance of the spliced reinforcement. This was then used to compare the structural performance of each remedial splice detail to the standard contact and non-contact lap splices. The wall splice specimens which contained non-contact lap splices with knock-out webs, s-shaped, and transverse reinforcement in the splice region achieved similar tensile capacities as the wall splice specimens with standard contact lap splices. Industry professionals have indicated that the installation of the remedial measures evaluated in this study would not affect the constructability of masonry assemblages in field situations. The splice detail with knock-out webs confined within the lap splice length was determined to be the most viable procedure as it can be installed to increase the resistance of non-contact lap splices in almost all construction situations. This remedial procedure was able to improve the tensile resistance of the lapped reinforcement by 63% compared to the wall splice specimens with standard non-contact lap splices.

In recent years, many researchers have investigated automated progress tracking for construction projects. These efforts range from 2D photo-feature extraction to 3D laser scanners and radio frequency identification (RFID) tags. A multi-sensor data fusion model that utilizes multiple sources of information would provide a better alternative than a single-source model for tracking project progress. However, many existing fusion models are based on data fusion at the sensor and object levels and are therefore incapable of capturing critical information regarding a number of activities and processes on a construction site, particularly those related to non-structural trades such as welding, inspection, and installation activities. In this research, a workflow based data fusion framework is developed for construction progress, quality and productivity assessment. The developed model is based on tracking construction activities as well as objects, in contrast to the existing sensor-based models that are focussed on tracking objects. Data sources include high frequency automated technologies including 3D imaging and ultra-wide band (UWB) positioning. Foreman reports, schedule information, and other data sources are included as well. Data fusion and management process workflow implementation via a distributed computing network and archiving using a cloud-based architecture are both illustrated. Validation was achieved using a detailed laboratory experimental program as well as an extensive field implementation project. The field implementation was conducted using five months of data acquired on the University of Waterloo Engineering VI construction project, yielding promising results. The data fusion processes of this research provide more accurate and more reliable progress and earned value estimates for construction project activities, while the developed data management processes enable the secure sharing and management of construction research data with the construction industry stakeholders as well as with researchers from other institutions.

In recent years, many researchers have investigated automated progress tracking for construction projects. These efforts range from 2D photo-feature extraction to 3D laser scanners and radio frequency identification (RFID) tags. A multi-sensor data fusion model that utilizes multiple sources of information would provide a better alternative than a single-source model for tracking project progress. However, many existing fusion models are based on data fusion at the sensor and object levels and are therefore incapable of capturing critical information regarding a number of activities and processes on a construction site, particularly those related to non-structural trades such as welding, inspection, and installation activities. In this research, a workflow based data fusion framework is developed for construction progress, quality and productivity assessment. The developed model is based on tracking construction activities as well as objects, in contrast to the existing sensor-based models that are focussed on tracking objects. Data sources include high frequency automated technologies including 3D imaging and ultra-wide band (UWB) positioning. Foreman reports, schedule information, and other data sources are included as well. Data fusion and management process workflow implementation via a distributed computing network and archiving using a cloud-based architecture are both illustrated. Validation was achieved using a detailed laboratory experimental program as well as an extensive field implementation project. The field implementation was conducted using five months of data acquired on the University of Waterloo Engineering VI construction project, yielding promising results. The data fusion processes of this research provide more accurate and more reliable progress and earned value estimates for construction project activities, while the developed data management processes enable the secure sharing and management of construction research data with the construction industry stakeholders as well as with researchers from other institutions.