Quantifying Advantages of Modular Construction: Waste Generation

The construction industry is a significant source of waste generation in any economy, producing various greenhouse gases, releasing harmful substances into the natural environment, and requiring large areas of land for processing, treatment, and landfilling. The emerging field of off-site prefabrication and assembly is perceived as a viable method to reduce waste and improve sustainability. However, there is a lack of quantifiable research into the difference between off-site prefabrication and on-site, conventional construction for numerous sustainability criteria. This paper focuses on modular construction as an off-site production system, where a framework to compare waste generation of modular and conventional, in-situ construction methods is proposed. This paper aims to quantify these differences. The framework relies on a comprehensive literature review to estimate the waste rates of building materials, which are then applied to realistic case studies in order to determine the differences in waste generation. Overall, modular construction reduces the overall weight of waste by up to 83.2%, for the cases considered. This corresponds to a 47.9% decrease in the cost of waste for large structures. Care must be taken to keep modular wastage as low as possible for a reduced cost of waste to be also present in smaller structures. This reduces the research gap of quantifying the waste differences between conventional and modular construction, and provides thoroughly researched waste rates for future research, while also improving the knowledge of industry stakeholders, informing them of the benefits of modular construction. This allows stakeholders to make more informed decisions when selecting an appropriate construction method

Modeling Fuel Use, Emissions and Mass of On-Road Construction Equipment through Monitoring Field Operations

Construction industry is considered as one of the largest contributors to fuel consumption and greenhouse gases (GHGs) emissions globally. Fuel use and emissions of construction equipment are normally estimated through simulation or conducting dynamometer tests in the laboratory which may not represent the real-world situations. In such models, fuel use and emissions rate are mainly estimated at macro scale, while the effect of operational conditions cannot be measured. There is also a lack of quantitative operational level fuel use and emissions reduction schemes in the construction industry despite the potential of significant cost saving by applying such strategies. This thesis presents an integrated data monitoring framework including instrumentation and experimentation procedures to monitor operations of construction equipment. It develops operational level models to estimate fuel use and emissions rate of on-road construction equipment through investigating the effect of operational and environmental variables. Using the automated data sensing system, this study also develops a comprehensive model to predict the weight of on-road construction vehicles and their carried payload as crucial parameter affecting fuel use and emissions rate. Three types of devices, portable emission measurement system (PEMS), GPS-aided inertial navigation system (GPS-INS) and engine data logger were employed to collect emissions rates, operational parameters and engine data of on-road construction vehicles. Models are developed through performing statistical regression and artificial neural network (ANN) analysis on the filtered data. The proposed models consider the engine specifications, operational factors and environmental parameters for estimating fuel use, emissions rate and weight of the vehicles. Based on the developed models, this study designs different schemes to improve fuel efficiency of construction equipment. As the main operational level strategy, optimal driving speed is proposed over other operational and environmental variables. Other factors, such as traffic conditions, effect of idling and equipment stop on fuel use and emissions production of equipment are also investigated. At equipment level, this thesis evaluates the impact of different engine tiers on fuel use and emissions rate through applying the developed models. It is found that adoption of high-tier engines leads to considerable savings on the operation costs of equipment

Dynamic Progress Monitoring of Masonry Construction through Mobile SLAM Mapping and As-Built Modeling

Traditional progress monitoring can be inaccurate and time-consuming, potentially causing time delay and cost overrun in construction projects. With development in technology, tools such as cameras, laser scanners, and building information modelling (BIM) have been used to overcome existing problems in the traditional approach. However, noise mitigation, extracting objects of interest from laser point clouds, and detailed progress measurement are problems that still exist. In this study a novel method of construction progress monitoring to measure the progress percentage is presented. The study integrates the simultaneous localization and mapping (SLAM) technique with as-built BIM to gather quick and accurate construction site progress information. The Hausdorff distance is utilized to extract objects of interest and filter out noise from site-scan data. As-built and as-planned BIM models are compared using Python and Dynamo, to obtain progress percentage. A case study was conducted on a residential building located in Sydney, Australia, to validate the application of the developed method. The outcome demonstrates that utilizing the SLAM technique and Hausdorff distance are effective in mitigating noise and extracting objects of interest from site-scan data, respectively. In addition, with an accuracy of 94.67 percent in estimation, the progress percentage was obtained based on material quantities. The obtained progress percentage could also be used in updating construction schedules and assisting decision-making