Toward improved flange bracing requirements for metal building frame systems

This research investigates the application of the AISC Direct Analysis Method for stability bracing design of columns, beams, beam-columns and frames. Emphasis is placed on out-of-plane flange bracing design in metal building frame systems. Potential improvements and extensions to the 2005 AISC Appendix 6 stability bracing provisions are studied and evaluated. The structural attributes considered include various general conditions encountered in practical metal building design: unequal brace spacing, unequal brace stiffness, nonprismatic member geometry, variable axial load or bending moment along the member length, cross-section double or single symmetry, combined bending and axial load, combined torsional and lateral bracing from girts/purlins with or without diagonal braces from these components to the inside flanges, load height, cross-section distortion, and non-rigid end boundary conditions. The research addresses both the simplification to basic bracing design rules as well as direct computation for more complex cases. The primary goal is improved assessment of the demands on flange bracing systems in metal building frames.M.S.Committee Chair: White, Donald; Committee Member: Leon, Roberto; Committee Member: Will, Kennet

Labour Productivity in the New Zealand Construction Industry: A Thorough Investigation

Productivity growth is strongly correlated to economic growth and increases in welfare. This fact also holds true at the industry level and is particularly true in the NZ construction industry, since productivity growth in this sector may have significant effects on the affordability of housing in the country. In recent years construction in NZ has been subjected to a series of reports that have either highlighted ‘failure’ to grow productivity or have exhorted the industry to improve its ‘poor performance’.  However thus far little by way of analysis has gone into the productivity figures that have been quoted, nor has much been done to explain and justify if or why these figures are correct or incorrect.This research seeks to deconstruct construction productivity figures in NZ and explain the patterns over recent years of ‘poor performance’ in comparison with other industries.  As such it will examine the nature of the NZ construction industry and analyse the historic statistics related to its labour productivity. This will provide an overall understanding of the sector as well as those extraneous factors that may have significant influences on the NZ construction sector.The research found that while factors influencing inputs of labour productivity measure such as labour and material costs remained stable, factors impacting the corresponding outputs such as house and land prices, value of work in Non-residential and Infrastructure construction grew significantly between 1997 and 2007. Given the positive skewing effect of standard economic indicators (inflation etc) on construction labour productivity figures, the relatively poor performance of construction is worrying for the industry. The paper concludes by demonstrating labour productivity in construction is significantly worse performing than previously suspected

A survey of data recovery on flash memory

In recent years, flash memory has become more widely used due to its advantages, such as fast data access, low power consumption, and high mobility. However, flash memory also has drawbacks that need to be overcome, such as erase-before-write, and the limitations of block deletion. In order to address this issue, the FTL (Flash Translation Layer) has been proposed with useful functionalities like address mapping, garbage collection, and wear-leveling. During the process of using, the data may be lost on power failure in the storage systems. In some systems, the data is very important. Thus recovery of data in the event of the system crash or a sudden power outage is of prime importance. This problem has attracted attention from researchers and many studies have been done. In this paper, we investigate previous studies on data recovery for flash memory from FTL processing solutions to PLR (Power Loss Recovery) solutions that have been proposed by authors in the conference proceeding, patents, or professional journals. This will provide a discussion of the proposed solutions to the data recovery in flash memory as well as an overview

Skip-Connected Neural Networks with Layout Graphs for Floor Plan Auto-Generation

With the advent of AI and computer vision techniques, the quest for automated and efficient floor plan designs has gained momentum. This paper presents a novel approach using skip-connected neural networks integrated with layout graphs. The skip-connected layers capture multi-scale floor plan information, and the encoder-decoder networks with GNN facilitate pixel-level probability-based generation. Validated on the MSD dataset, our approach achieved a 93.9 mIoU score in the 1st CVAAD workshop challenge. Code and pre-trained models are publicly available at https://github.com/yuntaeJ/SkipNet-FloorPlanGe

Greenhouse gas emissions from piggery and biogas digesters in the Red River Delta of Vietnam

High demand for pork consumption in Vietnam has led to a shift of pig production systems from smallholder to industrial-scale farms, particularly in the Red River Delta. This production intensification also produces massive manure and urine quantities, leading to water, air, and soil pollution. The use of biogas plants has been seen as efficient to achieve in the same time a decrease in pollution, and a provision of biogas resources and bio-organic fertilizers. However, increasing pig head density has been causing great pressure on biogas digesters, as their size is not big enough for treatments anymore. Inappropriate utilization and management of biogas digesters can not only cause losses from pig wastes, but also contributes to increase greenhouse gas (GHG) emissions such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). This case study aims to identify the role and contributions of biogas digesters to better manage the sources of GHG emissions from pig wastes for different types of pig farms. Four provinces of the Red River Delta were selected to test the pig waste management efficiency of biogas digesters and measure GHG emissions from these systems. The findings show that CO2, CH4 and N2O emission rates from pig manure are at least twice as much what is allowed under the Vietnam national technical regulation on ambient air quality. However, the GHGs emission rate does not significantly differ between smallholder and industrial-scale farms in the four surveyed provinces. Sampling position (between inside piggeries and outside the outlet of biogas digesters) did not affect significantly GHG emissions rate. These results confirm that the pig waste management of biogas digesters for both smallholder and industrial-scale pig farms is not efficient and that efforts need to be invested to mitigate GHG emissions in pig production. Reducing pig density per piggery is highly recommended. The modification of biogas digester structure to separate solid pig manure and urine should also be considered. Otherwise, the application of other alternative aerobic or anaerobic digestion technologies should also be encouraged and promoted. Biogas digesters in pig production have a significant role to play in Vietnam government’s mitigation strategies, as well as from the perspective of biosafety and animal husbandry policies

A Risk-Based Model to Select Delivery Methods for Highway Design and Construction Projects

Project delivery methods and contracting strategies allocate risk for design and construction between contractual parties. Selecting an appropriate delivery method is a critical to the success of highways and other infrastructure projects. The selection is often made early in the project development process. At the time of the decision, the owner and stakeholders often have little information and projects lack details to make accurate judgments about final project costs. Researcher and practitioners have striven to develop tools and techniques to support the project delivery decision, but most are qualitative approaches. In the last decade, transportation agencies have successfully applied cost and schedule risk analyses on their major projects, but they make project delivery decisions independently from these risk analyses. This dissertation capitalizes on the opportunity to apply quantitative risk analysis techniques to the highway project delivery selection process. This dissertation employs content analysis, survey research, univariate and multivariate analysis, and cross-impact analysis techniques to develop a risk-based model to quantitatively make informed delivery decisions. The dissertation follows a three-journal paper format. The first paper focuses on investigating the impact of risk on design-build (DB) Delivery. The DB method was selected as the main research setting for this paper because its risk allocation mechanism tends to be more complex than the traditional design-bid-build (DBB) or alternative construction manager/general contractor (CMGC) delivery method. The second paper advances understanding of how risk impacts the three fundamental delivery methods commonly used in highways: DBB, DB, and CMGC. Building upon these results, the third paper develops a risk-based model to quantify the impact of risk and delivery methods on project cost. It provides for an understanding of the optimal delivery method for an individual highway design and construction project. The dissertation contributes to theory by introducing a new approach to selecting project delivery methods. This dissertation also addresses a practical need to increase understanding of how risks impact project delivery selection in the highway design and construction industry

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning (DL) models. The considered DL models are the convolutional neural network (CNN) and long short-term memory network (LSTMN). The procedure is made over (i) setting the configuration for CNN and LSTMN, (ii) establishing the hyperparameter values of CNN and LSTMN models based on epoch, batch, optimizer, dropout, filters, and kernel, (iii) using eight data standardization methods to standardize the input data, and (iv) using the GS algorithm to search the optimal hyperparameters based on the mean absolute error (MAE) and mean absolute percent error (MAPE) indexes. The effectiveness of the proposed method is verified on the power load data of the Australian state of Queensland and Vietnamese Ho Chi Minh city. The simulation results show that the proposed data standardization methods are appropriate, except for the zero-mean and min-max methods

Evaluating the Economics of Construction and Demolition Waste Minimisation and Zero Waste in the New Zealand Construction Industry

Currently, up to 50% of construction and demolition (C&D) waste is disposed of in landfills contributing to significant environmental, social and economic costs to New Zealand. However, current understanding of C&D costs is poor both internationally and within New Zealand. This thesis addresses this deficit by developing a framework to evaluate the economics of C&D waste minimisation. An understanding gained from this research could help New Zealand develop appropriate strategies to address C&D waste issues. As the research problem is complex and wide-ranging, this study used a mixed-method approach. Semi-structured elite interviews with highly experienced construction personnel were used to identify factors affecting a C&D waste minimisation strategy. This also established the context of the economic evaluation framework. Economic modelling was subsequently employed to develop the economic evaluation framework. The framework was then applied on two case studies: 1) a development of a large education facility and 2) a refurbishment of a commercial office space. The study found that: 1. a C&D waste landfill/cleanfill charge of $150 per tonne can a) deter construction from disposing of waste; and b) force construction to rethink waste disposal 2. C&D waste minimisation can offer clients benefits including tangible returns (i.e. cost savings) and intangible potentials (i.e. increased reputation) 3. there are costs of implementing C&D waste minimisation - but benefits gained can outweigh such costs; and 4. the optimal rate of reduction for C&D waste in the non-residential projects studied was 71% - 78% Overall, this research has made a contribution to knowledge through the development of a robust economic evaluation framework. Moreover, the study has also provided an impetus for future work in C&D waste minimisation economics in New Zealand

Grid search of multilayer perceptron based on the walk-forward validation methodology

Multilayer perceptron neural network is one of the widely used method for load forecasting. There are hyperparameters which can be used to determine the network structure and used to train the multilayer perceptron neural network model. This paper aims to propose a framework for grid search model based on the walk-forward validation methodology. The training process will specify the optimal models which satisfy requirement for minimum of accuracy scores of root mean square error, mean absolute percentage error and mean absolute error. The testing process will evaluate the optimal models along with the other ones. The results indicated that the optimal models have accuracy scores near the minimum values. The US airline passenger and Ho Chi Minh city load demand data were used to verify the accuracy and reliability of the grid search framework