Advanced analysis of steel frame structures comprising non-compact sections

During the past decade, a significant amount of research has been conducted internationally with the aim of developing, implementing, and verifying "advanced analysis" methods suitable for non-linear analysis and design of steel frame structures. Application of these methods permits comprehensive assessment of the actual failure modes and ultimate strengths of structural systems in practical design situations, without resort to simplified elastic methods of analysis and semi-empirical specification equations. Advanced analysis has the potential to extend the creativity of structural engineers and simplify the design process, while ensuring greater economy and more uniform safety with respect to the ultimate limit state. The application of advanced analysis methods has previously been restricted to steel frames comprising only members with compact cross-sections that are not subject to the effects of local buckling. This precluded the use of advanced analysis from the design of steel frames comprising a significant proportion of the most commonly used Australian sections, which are non-compact and subject to the effects of local buckling. This thesis contains a detailed description of research conducted over the past three years in an attempt to extend the scope of advanced analysis by developing methods that include the effects of local buckling in a non-linear analysis formulation, suitable for practical design of steel frames comprising non-compact sections. Two alternative concentrated plasticity formulations are presented in this thesis: the refined plastic hinge method and the pseudo plastic zone method. Both methods implicitly account for the effects of gradual cross-sectional yielding, longitudinal spread of plasticity, initial geometric imperfections, residual stresses, and local buckling. The accuracy and precision of the methods for the analysis of steel frames comprising non-compact sections has been established by comparison with a comprehensive range of analytical benchmark frame solutions. Both the refined plastic hinge and pseudo plastic zone methods are more accurate and precise than the conventional individual member design methods based on elastic analysis and specification equations. For example, the pseudo plastic zone method predicts the ultimate strength of the analytical benchmark frames with an average conservative error of less than one percent, and has an acceptable maximum unconservati_ve error of less than five percent. The pseudo plastic zone model can allow the design capacity to be increased by up to 30 percent for simple frames, mainly due to the consideration of inelastic redistribution. The benefits may be even more significant for complex frames with significant redundancy, which provides greater scope for inelastic redistribution. The analytical benchmark frame solutions were obtained using a distributed plasticity shell finite element model. A detailed description of this model and the results of all the 120 benchmark analyses are provided. The model explicitly accounts for the effects of gradual cross-sectional yielding, longitudinal spread of plasticity, initial geometric imperfections, residual stresses, and local buckling. Its accuracy was verified by comparison with a variety of analytical solutions and the results of three large-scale experimental tests of steel frames comprising non-compact sections. A description of the experimental method and test results is also provided.

Empirical investigation of interactive highway safety design model accident prediction algorithm : Rural intersections

One major gap in transportation system safety management is the ability to assess the safety ramifications of design changes for both new road projects and modifications to existing roads. To fulfill this need, FHWA and its many partners are developing a safety forecasting tool, the Interactive Highway Safety Design Model (IHSDM). The tool will be used by roadway design engineers, safety analysts, and planners throughout the United States. As such, the statistical models embedded in IHSDM will need to be able to forecast safety impacts under a wide range of roadway configurations and environmental conditions for a wide range of driver populations and will need to be able to capture elements of driving risk across states. One of the IHSDM algorithms developed by FHWA and its contractors is for forecasting accidents on rural road segments and rural intersections. The methodological approach is to use predictive models for specific base conditions, with traffic volume information as the sole explanatory variable for crashes, and then to apply regional or state calibration factors and accident modification factors (AMFs) to estimate the impact on accidents of geometric characteristics that differ from the base model conditions. In the majority of past approaches, AMFs are derived from parameter estimates associated with the explanatory variables. A recent study for FHWA used a multistate database to examine in detail the use of the algorithm with the base model-AMF approach and explored alternative base model forms as well as the use of full models that included nontraffic-related variables and other approaches to estimate AMFs. That research effort is reported. The results support the IHSDM methodology.

Feasibility of "subjective" engineering assessments of road safety improvements : Bayesian analysis development

Regional safety program managers face a daunting challenge in the attempt to reduce deaths, injuries, and economic losses that result from motor vehicle crashes. This difficult mission is complicated by the combination of a large perceived need, small budget, and uncertainty about how effective each proposed countermeasure would be if implemented. A manager can turn to the research record for insight, but the measured effect of a single countermeasure often varies widely from study to study and across jurisdictions. The challenge of converting widespread and conflicting research results into a regionally meaningful conclusion can be addressed by incorporating "subjective" information into a Bayesian analysis framework. Engineering evaluations of crashes provide the subjective input on countermeasure effectiveness in the proposed Bayesian analysis framework. Empirical Bayes approaches are widely used in before-and-after studies and "hot-spot" identification; however, in these cases, the prior information was typically obtained from the data (empirically), not subjective sources. The power and advantages of Bayesian methods for assessing countermeasure effectiveness are presented. Also, an engineering evaluation approach developed at the Georgia Institute of Technology is described. Results are presented from an experiment conducted to assess the repeatability and objectivity of subjective engineering evaluations. In particular, the focus is on the importance, methodology, and feasibility of the subjective engineering evaluation for assessing countermeasures.

An empirical test of causal relationships of factors affecting ICT adoption for building project management : an Indian SME case study

Purpose - Building project management (BPM) requires effective coordination and collaboration between multiple project team organisations which can be achieved by real time information flow between all participants. In the present scenario, this can be achieved by the use of information communication technologies (ICT). The purpose of this paper is to present part of a research project conducted to study the causal relationships between factors affecting ICT adoption for BPM by small and medium enterprises. Design/methodology/approach - This paper discusses structural equation modelling (SEM) analysis conducted to test the causal relationships between quantitative factors. Data for quantitative analysis were gathered through a questionnaire survey conducted in the Indian construction industry. Findings - SEM analysis results help in demonstrating that an increased and matured use of ICT for general administration within the organisation would lead to: an improved ICT infrastructure within the organisation; development of electronic databases; and a staff that is confident of using information technology (IT) tools. In such a scenario, staff would use advanced software and IT technologies for project management (PM) processes and that would lead to an increased adoption of ICT for PM processes. But, for general administration also, ICT adoption would be enhanced if the organisation is interacting more with geographically separated agencies and senior management perceives that significant benefits would accrue by adoption of ICT. All the factors are inter-related and their effect cannot be maximized in isolation. Originality/value - The results provide direction to building project managements for strategically adopting the effective use of ICT within their organisations and for BPM general.

Ownership structure and business evaluation practices in family SMEs : evidence from empirical research in Italy

This paper presents early results from a pilot project which aims to investigate the relationship between proprietary structure of small and medium- sized Italian family firms and their owners’ orientation towards a “business evaluation process”. Evidence from many studies point out the importance of family business in a worldwide economic environment: in Italy 93% of the businesses are represented by family firms; 98% of them have less than 50 employees (Italian Association of Family Firms, 2004) so we judged family SMEs as a relevant field of investigation. In this study we assume a broad definition of family business as “a firm whose control (50% of shares or voting rights) is closely held by the members of the same family” (Corbetta,1995). “Business evaluation process” is intended here both as “continuous evaluation process” (which is the expression of a well developed managerial attitude) or as an “immediate valuation” (i.e. in the case of new shareholder’s entrance, share exchange among siblings, etc). We set two hypotheses to be tested in this paper: the first is “quantitative” and aims to verify whether the number of owners (independent variable) in a family firm is positively correlated to the business evaluation process. If a family firm is led by only one subject, it is more likely that personal values, culture and feelings may affect his choices more than “purely economic opportunities”; so there is less concern about monitoring economic performance or about the economic value of the firm. As the shareholders’ number increases, economic aspects in managing the firm grow in importance over the personal values and "value orientation" acquires a central role. The second hypothesis investigates if and to what extent the presence of “non- family members” among the owners affects their orientation to the business evaluation process. The “Cramer’s V” test has been used to test the hypotheses; both were not confirmed from these early results; next steps will lead to make an inferential analysis on a representative sample of the population.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Spatial analysis of notified dengue fever infections

This study aimed to investigate the spatial clustering and dynamic dispersion of dengue incidence in Queensland, Australia. We used Moran’s I statistic to assess the spatial autocorrelation of reported dengue cases. Spatial empirical Bayes smoothing estimates were used to display the spatial distribution of dengue in postal areas throughout Queensland. Local indicators of spatial association (LISA) maps and logistic regression models were used to identify spatial clusters and examine the spatio-temporal patterns of the spread of dengue. The results indicate that the spatial distribution of dengue was clustered during each of the three periods of 1993–1996, 1997–2000 and 2001–2004. The high-incidence clusters of dengue were primarily concentrated in the north of Queensland and low-incidence clusters occurred in the south-east of Queensland. The study concludes that the geographical range of notified dengue cases has significantly expanded in Queensland over recent years.

Essays on irrigation development, farm production and unaccounted costs : theory and empirical evidence

A review of the literature related to issues involved in irrigation induced agricultural development (IIAD) reveals that: (1) the magnitude, sensitivity and distribution of social welfare of IIAD is not fully analysed; (2) the impacts of excessive pesticide use on farmers’ health are not adequately explained; (3) no analysis estimates the relationship between farm level efficiency and overuse of agro-chemical inputs under imperfect markets; and (4) the method of incorporating groundwater extraction costs is misleading. This PhD thesis investigates these issues by using primary data, along with secondary data from Sri Lanka. The overall findings of the thesis can be summarised as follows. First, the thesis demonstrates that Sri Lanka has gained a positive welfare change as a result of introducing new irrigation technology. The change in the consumer surplus is Rs.48,236 million, while the change in the producer surplus is Rs. 14,274 millions between 1970 and 2006. The results also show that the long run benefits and costs of IIAD depend critically on the magnitude of the expansion of the irrigated area, as well as the competition faced by traditional farmers (agricultural crowding out effects). The traditional sector’s ability to compete with the modern sector depends on productivity improvements, reducing production costs and future structural changes (spillover effects). Second, the thesis findings on pesticides used for agriculture show that, on average, a farmer incurs a cost of approximately Rs. 590 to 800 per month during a typical cultivation period due to exposure to pesticides. It is shown that the value of average loss in earnings per farmer for the ‘hospitalised’ sample is Rs. 475 per month, while it is approximately Rs. 345 per month for the ‘general’ farmers group during a typical cultivation season. However, the average willingness to pay (WTP) to avoid exposure to pesticides is approximately Rs. 950 and Rs. 620 for ‘hospitalised’ and ‘general’ farmers’ samples respectively. The estimated percentage contribution for WTP due to health costs, lost earnings, mitigating expenditure, and disutility are 29, 50, 5 and 16 per cent respectively for hospitalised farmers, while they are 32, 55, 8 and 5 per cent respectively for ‘general’ farmers. It is also shown that given market imperfections for most agricultural inputs, farmers are overusing pesticides with the expectation of higher future returns. This has led to an increase in inefficiency in farming practices which is not understood by the farmers. Third, it is found that various groundwater depletion studies in the economics literature have provided misleading optimal water extraction quantity levels. This is due to a failure to incorporate all production costs in the relevant models. It is only by incorporating quality changes to quantity deterioration, that it is possible to derive socially optimal levels. Empirical results clearly show that the benefits per hectare per month considering both the avoidance costs of deepening agro-wells by five feet from the existing average, as well as the avoidance costs of maintaining the water salinity level at 1.8 (mmhos/Cm), is approximately Rs. 4,350 for farmers in the Anuradhapura district and Rs. 5,600 for farmers in the Matale district.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

A comparative analysis of business analysis (BA) and business process management (BPM) capabilities

Many initiatives to improve Business processes are emerging. The essential roles and contributions of Business Analyst (BA) and Business Process Management (BPM) professionals to such initiatives have been recognized in literature and practice. The roles and responsibilities of a BA or BPM practitioner typically require different skill-sets; however these differences are often vague. This vagueness creates much confusion in practice and academia. While both the BA and BPM communities have made attempts to describe their domains through capability defining empirical research and developments of Bodies of knowledge, there has not yet been any attempt to identify the commonality of skills required and points of uniqueness between the two professions. This study aims to address this gap and presents the findings of a detailed content mapping exercise (using NVivo as a qualitative data analysis tool) of the International Institution of Business Analysis (IIBA®) Guide to the Business Analysis Body of Knowledge (BABOK® Guide) against core BPM competency and capability frameworks.

Empirical minimization

We investigate the behavior of the empirical minimization algorithm using various methods. We first analyze it by comparing the empirical, random, structure and the original one on the class, either in an additive sense, via the uniform law of large numbers, or in a multiplicative sense, using isomorphic coordinate projections. We then show that a direct analysis of the empirical minimization algorithm yields a significantly better bound, and that the estimates we obtain are essentially sharp. The method of proof we use is based on Talagrand’s concentration inequality for empirical processes.

Analysis of productive performance of crop and animal production systems : an integrated analytical framework

This article presents a two-stage analytical framework that integrates ecological crop (animal) growth and economic frontier production models to analyse the productive efficiency of crop (animal) production systems. The ecological crop (animal) growth model estimates "potential" output levels given the genetic characteristics of crops (animals) and the physical conditions of locations where the crops (animals) are grown (reared). The economic frontier production model estimates "best practice" production levels, taking into account economic, institutional and social factors that cause farm and spatial heterogeneity. In the first stage, both ecological crop growth and economic frontier production models are estimated to calculate three measures of productive efficiency: (1) technical efficiency, as the ratio of actual to "best practice" output levels; (2) agronomic efficiency, as the ratio of actual to "potential" output levels; and (3) agro-economic efficiency, as the ratio of "best practice" to "potential" output levels. Also in the first stage, the economic frontier production model identifies factors that determine technical efficiency. In the second stage, agro-economic efficiency is analysed econometrically in relation to economic, institutional and social factors that cause farm and spatial heterogeneity. The proposed framework has several important advantages in comparison with existing proposals. Firstly, it allows the systematic incorporation of all physical, economic, institutional and social factors that cause farm and spatial heterogeneity in analysing the productive performance of crop and animal production systems. Secondly, the location-specific physical factors are not modelled symmetrically as other economic inputs of production. Thirdly, climate change and technological advancements in crop and animal sciences can be modelled in a "forward-looking" manner. Fourthly, knowledge in agronomy and data from experimental studies can be utilised for socio-economic policy analysis. The proposed framework can be easily applied in empirical studies due to the current availability of ecological crop (animal) growth models, farm or secondary data, and econometric software packages. The article highlights several directions of empirical studies that researchers may pursue in the future.

Analysis for the use of cumulative plots for travel time estimation on signalized network

This paper provides fundamental understanding for the use of cumulative plots for travel time estimation on signalized urban networks. Analytical modeling is performed to generate cumulative plots based on the availability of data: a) Case-D, for detector data only; b) Case-DS, for detector data and signal timings; and c) Case-DSS, for detector data, signal timings and saturation flow rate. The empirical study and sensitivity analysis based on simulation experiments have observed the consistency in performance for Case-DS and Case-DSS, whereas, for Case-D the performance is inconsistent. Case-D is sensitive to detection interval and signal timings within the interval. When detection interval is integral multiple of signal cycle then it has low accuracy and low reliability. Whereas, for detection interval around 1.5 times signal cycle both accuracy and reliability are high.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

The relation between work motivation and project management success : an empirical investigation

The content and context of work significantly influences an employees’ satisfaction. While managers see work motivation as a tool to engage the employees so that they perform better, academicians value work motivation for its contribution to human behaviour. Though the relationship between employee motivation and project success has been extensively covered in the literature, more research focusing on the nature of job design on project success may have been wanting. We address this gap through this study. The present study contributes to the extant literature by suggesting an operational framework of work motivation for project—based organizations. We are also advancing the conceptual understanding of this variable by understanding how the different facets of work motivation have a differing impact of the various parameters of project performance. A survey instrument using standardized scales of work motivation and project success was used. 199 project workers from various industries completed the survey. We first ‘operationalized’ the definition of work motivation for the purpose of our study through a principal component analysis of work motivation items. We obtained a five factor structure that had items pertaining to employee development, work climate, goal clarity, and job security. We then performed a Pearson’s correlation analysis which revealed moderate to significant relationship between project outcomes ad work climate; project outcomes & employee development. In order to establish a causality between work motivation and project management success, we employed linear regression analysis. The results show that work climate is a significant predictor of client satisfaction, while it moderately influences the project quality. Further, bringing in objectivity to project work is important for a successful implementation.