Safety leaders' perceptions of safety culture in a large Australian construction organisation

Safety culture in the construction industry is a growing research area. The unique nature of construction industry works – being project-based, varying in size and focus, and relying on a highly transient subcontractor workforce – means that safety culture initiatives cannot be easily translated from other industries. This paper reports on the first study in a three year collaborative industry and university research project focusing on safety culture practices and development in one of Australia’s largest global construction organisations. The first round of a modified Delphi method is reported, and describes the insights gained from 41 safety leaders’ perceptions and understandings of safety culture within the organisation. In-depth, semi-structured interviews were conducted, and will be followed by a quantitative perception survey with the same sample. Participants included Senior Executives, Corporate Managers, Project Managers, Safety Managers and Site Supervisors. Leaders’ definitions and descriptions of safety culture were primarily action-oriented and some confusion was evident due to the sometimes implicit nature of culture in organisations. Leadership was identified as a key factor for positive safety culture in the organisation, and there was an emphasis on leaders demonstrating commitment to safety, and being visible to the project-based workforce. Barriers to safety culture improvement were also identified, with managers raising diverse issues such as the transient subcontractor workforce and the challenge of maintaining safety as a priority in the absence of safety incidents, under high production pressures. This research is unique in that it derived safety culture descriptions from key stakeholders within the organisation, as opposed to imposing traditional conceptualisations of safety culture that are not customised for the organisation or the construction industry more broadly. This study forms the foundation for integrating safety culture theory and practice in the construction industry, and will be extended upon in future studies within the research program.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns

Almost all metapopulation modelling assumes that connectivity between patches is only a function of distance, and is therefore symmetric. However, connectivity will not depend only on the distance between the patches, as some paths are easy to traverse, while others are difficult. When colonising organisms interact with the heterogeneous landscape between patches, connectivity patterns will invariably be asymmetric. There have been few attempts to theoretically assess the effects of asymmetric connectivity patterns on the dynamics of metapopulations. In this paper, we use the framework of complex networks to investigate whether metapopulation dynamics can be determined by directly analysing the asymmetric connectivity patterns that link the patches. Our analyses focus on “patch occupancy” metapopulation models, which only consider whether a patch is occupied or not. We propose three easily calculated network metrics: the “asymmetry” and “average path strength” of the connectivity pattern, and the “centrality” of each patch. Together, these metrics can be used to predict the length of time a metapopulation is expected to persist, and the relative contribution of each patch to a metapopulation’s viability. Our results clearly demonstrate the negative effect that asymmetry has on metapopulation persistence. Complex network analyses represent a useful new tool for understanding the dynamics of species existing in fragmented landscapes, particularly those existing in large metapopulations.

Resilience of a corrupt police network : the first and second jokes in Queensland

Resilient organised crime groups survive and prosper despite law enforcement activity, criminal competition and market forces. Corrupt police networks, like any other crime network, must contain resiliency characteristics if they are to continue operation and avoid being closed down through detection and arrest of their members. This paper examines the resilience of a large corrupt police network, namely The Joke which operated in the Australian state of Queensland for a number of decades. The paper uses social network analysis tools to determine the resilient characteristics of the network. This paper also assumes that these characteristics will be different to those of mainstream organised crime groups because the police network operates within an established policing agency rather than as an independent entity hiding within the broader community.

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.

Loop closure detection on a suburban road network using a continuous appearance-based trajectory

This paper presents a novel technique for performing SLAM along a continuous trajectory of appearance. Derived from components of FastSLAM and FAB-MAP, the new system dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM) augments appearancebased place recognition with particle-filter based ‘pose filtering’ within a probabilistic framework, without calculating global feature geometry or performing 3D map construction. For loop closure detection CAT-SLAM updates in constant time regardless of map size. We evaluate the effectiveness of CAT-SLAM on a 16km outdoor road network and determine its loop closure performance relative to FAB-MAP. CAT-SLAM recognizes 3 times the number of loop closures for the case where no false positives occur, demonstrating its potential use for robust loop closure detection in large environments.

The development of a conceptual framework for a district 4-Year Pavement Management Plan, implementation of network-level project screening process

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between preservation needs and available funding. Funding levels are not adequate to meet the preservation needs of the roadway network; therefore projects listed in the 4-Year Pavement Management Plan must be ranked to determine which projects should be funded now and which can be postponed until a later year. Currently, each district uses locally developed methods to prioritize projects. These ranking methods have relied on less formal qualitative assessments based on engineers’ subjective judgment. It is important for TxDOT to have a 4-Year Pavement Management Plan that uses a transparent, rational project ranking process. The objective of this study is to develop a conceptual framework that describes the development of the 4-Year Pavement Management Plan. It can be largely divided into three Steps; 1) Network-Level project screening process, 2) Project-Level project ranking process, and 3) Economic Analysis. A rational pavement management procedure and a project ranking method accepted by districts and the TxDOT administration will maximize efficiency in budget allocations and will potentially help improve pavement condition. As a part of the implementation of the 4-Year Pavement Management Plan, the Network-Level Project Screening (NLPS) tool including the candidate project identification algorithm and the preliminary project ranking matrix was developed. The NLPS has been used by the Austin District Pavement Engineer (DPE) to evaluate PMIS (Pavement Management Information System) data and to prepare a preliminary list of candidate projects for further evaluation.

Modeling train delays in urban networks

The reliability of urban passenger trains is a critical performance measure for passenger satisfaction and ultimately market share. A delay to one train in a peak period can have a severe effect on the schedule adherence of other trains. This paper presents an analytically based model to quantify the expected positive delay for individual passenger trains and track links in an urban rail network. The model specifically addresses direct delay to trains, knock-on delays to other trains, and delays at scheduled connections. A solution to the resultant system of equations is found using an iterative refinement algorithm. Model validation, which is carried out using a real-life suburban train network consisting of 157 trains, shows the model estimates to be on average within 8% of those obtained from a large scale simulation. Also discussed, is the application of the model to assess the consequences of increased scheduled slack time as well as investment strategies designed to reduce delay.

Large area noise evaluation

A software tool (DRONE) has been developed to evaluate road traffic noise in a large area with the consideration of network dynamic traffic flow and the buildings. For more precise estimation of noise in urban network where vehicles are mainly in stop and go running conditions, vehicle sound power level (for acceleration/deceleration cruising and ideal vehicle) is incorporated in DRONE. The calculation performance of DRONE is increased by evaluating the noise in two steps of first estimating the unit noise database and then integrating it with traffic simulation. Details of the process from traffic simulation to contour maps are discussed in the paper and the implementation of DRONE on Tsukuba city is presented.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Development of network-level project screening methods supporting the 4-Year pavement management plan in Texas

The Texas Department of Transportation (TxDOT) is concerned about the widening gap between pavement preservation needs and available funding. Thus, the TxDOT Austin District Pavement Engineer (DPE) has investigated methods to strategically allocate available pavement funding to potential projects that improve the overall performance of the District and Texas highway systems. The primary objective of the study presented in this paper is to develop a network-level project screening and ranking method that supports the Austin District 4-year pavement management plan development. The study developed candidate project selection and ranking algorithms that evaluated pavement conditions of each project candidate using data contained in the Pavement Management Information system (PMIS) database and incorporated insights from Austin District pavement experts; and implemented the developed method and supporting algorithm. This process previously required weeks to complete, but now requires about 10 minutes including data preparation and running the analysis algorithm, which enables the Austin DPE to devote more time and resources to conducting field visits, performing project-level evaluation and testing candidate projects. The case study results showed that the proposed method assisted the DPE in evaluating and prioritizing projects and allocating funds to the right projects at the right time.

Larger or broader : performance implications of size and diversity of the knowledge worker's egocentric network. [更多或更广：知识员工个人中心网络大小和 多样性对绩效的影响]

Management scholars and practitioners emphasize the importance of the size and diversity of a knowledge worker's social network. Constraints on knowledge workers’ time and energy suggest that more is not always better. Further, why and how larger networks contribute to valuable outcomes deserves further understanding. In this study, we offer hypotheses to shed insight on the question of the diminishing returns of large networks and the specific form of network diversity that may contribute to innovative performance among knowledge workers. We tested our hypotheses using data collected from 93 R&D engineers in a Sino-German automobile electronics company located in China. Study findings identified an inflection point, confirming our hypothesis that the size of the knowledge worker's egocentric network has an inverted U-shaped effect on job performance. We further demonstrate that network dispersion richness (the number of cohorts that the focal employee has connections to) rather than network dispersion evenness (equal distribution of ties across the cohorts) has more influence on the knowledge worker's job performance. Additionally, we found that the curvilinear effect of network size is fully mediated by network dispersion richness. Implications for future research on social networks in China and Western contexts are discussed.

Network interactions and performance of a multi-function IEC 61850 process bus

New substation technology, such as non-conventional instrument transformers,and a need to reduce design and construction costs, are driving the adoption of Ethernet based digital process bus networks for high voltage substations. Protection and control applications can share a process bus, making more efficient use of the network infrastructure. This paper classifies and defines performance requirements for the protocols used in a process bus on the basis of application. These include GOOSE, SNMP and IEC 61850-9-2 sampled values. A method, based on the Multiple Spanning Tree Protocol (MSTP) and virtual local area networks, is presented that separates management and monitoring traffic from the rest of the process bus. A quantitative investigation of the interaction between various protocols used in a process bus is described. These tests also validate the effectiveness of the MSTP based traffic segregation method. While this paper focusses on a substation automation network, the results are applicable to other real-time industrial networks that implement multiple protocols. High volume sampled value data and time-critical circuit breaker tripping commands do not interact on a full duplex switched Ethernet network, even under very high network load conditions. This enables an efficient digital network to replace a large number of conventional analog connections between control rooms and high voltage switchyards.