Natural disaster risk-informed for a better public infrastructure asset planning in Indonesian Central Government

The natural disasters incident that frequently hit Indonesia are floods, severe droughts, tsunamis, earth-quakes, volcano, eruptions, landslides, windstorm and forest fires. The impact of those natural disasters are significantly severe and affecting the quality of life of the community due to the breakdown of the public as-sets as one source to deliver public services. This paper is aimed to emphasis the importance of natural disaster risk-informed in relation to public asset management in Indonesian Central Government, particularly in asset planning stage where asset decision is made as the gate into the whole public asset management processes. A Case study in the Ministry of Finance Indonesia as the central government public asset manager and in 5 (five) line ministries/governmental agencies as public asset users was used as the approach to achieved the research objective. The case study devoured three data collection techniques i.e. interviews, observations and document archival which will be analysed by a content analysis approach. The result of the study indicates that Indonesian geographical position exposing many of public infra-structure assets as a high vulnerability to natural disasters. Information on natural-disaster trends and predictions to identify and measure the risks are available, however, such information are not utilise and integrated to the process of public infrastructure asset planning as the gate to the whole public asset management processes. Therefore, in order to accommodate and incorporate this natural disaster risk-information into public asset management processes, particularly in public asset planning, a public asset performance measurements framework should be adopted and applied in the process as one sources in making decision for infrastructure asset planning. Findings from this study provide useful input for the Ministry of Finance as public asset manager, scholars and private asset management practitioners in Indonesia to establish natural disaster risks awareness in public infrastructure asset management processes.

Assessing resilience of water resources systems under the impacts of climate change

This paper presents an approach to assess the resilience of a water supply system under the impacts of climate change. Changes to climate characteristics such as rainfall, evapotranspiration and temperature can result in changes to the global hydrological cycle and thereby adversely impact on the ability of water supply systems to meet service standards in the future. Changes to the frequency and characteristics of floods and droughts as well as the quality of water provided by groundwater and surface water resources are the other consequences of climate change that will affect water supply system functionality. The extent and significance of these changes underline the necessity for assessing the future functionality of water supply systems under the impacts of climate change. Resilience can be a tool for assessing the ability of a water supply system to meet service standards under the future climate conditions. The study approach is based on defining resilience as the ability of a system to absorb pressure without going into failure state as well as its ability to achieve an acceptable level of function quickly after failure. In order to present this definition in the form of a mathematical function, a surrogate measure of resilience has been proposed in this paper. In addition, a step-by-step approach to estimate resilience of water storage reservoirs is presented. This approach will enable a comprehensive understanding of the functioning of a water storage reservoir under future climate scenarios and can also be a robust tool to predict future challenges faced by water supply systems under the consequence of climate change.

An integrated framework for assessing community resilience in disaster management

Climate change is predicted to increase the frequency and severity of extreme weather events which pose significant challenges to the ability of government and other relief agencies to plan for, cope with and respond to disasters. Consequently, it is important that communities in climate sensitive and potential disaster prone areas strengthen their resilience to natural disasters in order to expeditiously recover from potential disruptions and damage caused by disasters. Building self reliance and, particularly in the immediate aftermath of a disaster, can facilitate short-term and long-term community recovery. To build stronger and more resilient communities, it is essential to have a better understanding of their current resilience capabilities by assessing areas of strength, risks and vulnerabilities so that their strengths can be enhanced and the risks and vulnerability can be appropriately addressed and mitigated through capacity building programs. While a number of conceptual frameworks currently exist to assess the resilience level of communities to disasters, they have tended to differ on their emphasis, scope and definition of what constitutes community resilience and how community resilience can be most effectively and accurately assessed. These limitations are attributed to the common approach of viewing community resilience through a mono-disciplinary lens. To overcome this, this paper proposes an integrated conceptual framework that takes into account the complex interplay of environmental, social, governance, infrastructure and economic attributes associated with community resilience. The framework can be operationalised using a range of resilience indicators to suit the nature of a disaster and the specific characteristics of a study region.

An approach for identifying the limit states of resilience of a water supply system

This paper presents an approach for identifying the limit states of resilience in a water supply system when influenced by different types of pressure (disturbing) forces. Understanding of systemic resilience facilitates identification of the trigger points for early managerial action to avoid further loss of ability to provide satisfactory service availability when the ability to supply water is under pressure. The approach proposed here is to illustrate the usefulness of a surrogate measure of resilience depicted in a three dimensional space encompassing independent pressure factors. That enables visualisation of the transition of the system-state (resilience) between high to low resilience regions and acts as an early warning trigger for decision-making. The necessity of a surrogate measure arises as a means of linking resilience to the identified pressures as resilience cannot be measured directly. The basis for identifying the resilience surrogate and exploring the interconnected relationships within the complete system, is derived from a meta-system model consisting of three nested sub-systems representing the water catchment and reservoir; treatment plant; and the distribution system and end-users. This approach can be used as a framework for assessing levels of resilience in different infrastructure systems by identifying a surrogate measure and its relationship to relevant pressures acting on the system.

Innovative methods for assessing viability of diversification into new markets : a study based on an engineering consultancy

This study resulted in the development of a decision making tool for engineering consultancies looking to diversify into new markets. It reviewed existing decision tools used by contractor's entering new markets to develop a bespoke tool for engineering consultants to establish more rigor around the decision making process rather than rely purely on the intuition of company executives. The tool can be used for developing medium and long term company strategies or as a quick and efficient way to assess the viability of new market opportunities when they arise. A combination of Delphi and Analytical Hierarchy Process was selected as the basis of the decision theory.

Project management office a knowledge broker in project-based organisations

Current research into project management offices (PMOs) has stressed the PMOs' potential to act as knowledge brokers between projects, and between project and top management. Nonetheless, the literature does not provide sufficient evidence of the brokering role of PMOs. The research reported here aims to examine PMO's functions from a knowledge sharing perspective and explore whether or not these functions reflect the knowledge sharing needs of project managers (PMs). These issues are investigated through a cross-case analysis of seven organisations. The main contribution is insight into how PMs share knowledge and awareness of the need to structure PMOs to align with PMs' nature, needs and expectations in order to improve knowledge sharing in PBOs. Finally, some practical steps for helping PMOs to better adapt their functions to the needs of PMs and their learning and knowledge sharing style are proposed.

A new method for quantifying the contribution of different critical agents on railway bridge deterioration

Railway Bridges deteriorate over time due to different critical factors including, flood, wind, earthquake, collision, and environment factors, such as corrosion, wear, termite attack, etc. In current practice, the contributions of the critical factors, towards the deterioration of railway bridges, which show their criticalities, are not appropriately taken into account. In this paper, a new method for quantifying the criticality of these factors will be introduced. The available knowledge as well as risk analyses conducted in different Australian standards and developed for bridge-design will be adopted. The analytic hierarchy process (AHP) is utilized for prioritising the factors. The method is used for synthetic rating of railway bridges developed by the authors of this paper. Enhancing the reliability of predicting the vulnerability of railway bridges to the critical factors, will be the significant achievement of this research.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

On-line Demand Management of Low Voltage Residential Distribution Networks in Smart Grids

A novel intelligent online demand management system is discussed in this chapter for peak load management in low voltage residential distribution networks based on the smart grid concept. The discussed system also regulates the network voltage, balances the power in three phases and coordinates the energy storage within the network. This method uses low cost controllers, with two-way communication interfaces, installed in costumers’ premises and at distribution transformers to manage the peak load while maximizing customer satisfaction. A multi-objective decision making process is proposed to select the load(s) to be delayed or controlled. The efficacy of the proposed control system is verified by a MATLAB-based simulation which includes detailed modeling of residential loads and the network.

Spatially-explicit modelling of grassland classes - an improved method of integrating a climate-based classification model with interpolated climate surfaces

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterized by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the IDW approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were "cool temperate-arid temperate zonal semi-desert", "cool temperate-humid forest steppe and deciduous broad-leaved forest", "temperate-extra-arid temperate zonal desert", and "frigid per-humid rain tundra and alpine meadow". The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies' decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities which will help to prevent overgrazing and land degradation.

Empirical study to investigate the difficulties of implementing safety practices in the repair and maintenance sector in Hong Kong

Repair, maintenance, minor alteration, and addition work (RMAA) has become more and more important in developed societies, but its safety performance is alarming. For example, RMAA projects accounted for 53.2% of the total construction market and the percentage of RMAA accidents to all construction accidents in the Hong Kong Special Administrative Region (HKSAR) increased considerably in 2007. The RMAA sector has a huge potential for safety improvement. This study aims to explore and evaluate the difficulties of implementing safety practices in RMAA work. The mixed methods approach was adopted, and semistructured interviews and a two-round Delphi survey were conducted for the data collection. Major difficulties were identified, including limited safety resources for small and medium enterprises (SMEs), difficulty in changing the mindset of RMAA workers, and difficulty in performing safety supervision. These obstacles for implementing safety practices in the RMAA sector, if successfully removed, could significantly improve the safety performance of the RMAA sector and the construction industry as a whole.

Trust in merged ERP and open data schemes in the “Cloud”

Enterprise resource planning (ERP) systems are rapidly being combined with “big data” analytics processes and publicly available “open data sets”, which are usually outside the arena of the enterprise, to expand activity through better service to current clients as well as identifying new opportunities. Moreover, these activities are now largely based around relevant software systems hosted in a “cloud computing” environment. However, the over 50- year old phrase related to mistrust in computer systems, namely “garbage in, garbage out” or “GIGO”, is used to describe problems of unqualified and unquestioning dependency on information systems. However, a more relevant GIGO interpretation arose sometime later, namely “garbage in, gospel out” signifying that with large scale information systems based around ERP and open datasets as well as “big data” analytics, particularly in a cloud environment, the ability to verify the authenticity and integrity of the data sets used may be almost impossible. In turn, this may easily result in decision making based upon questionable results which are unverifiable. Illicit “impersonation” of and modifications to legitimate data sets may become a reality while at the same time the ability to audit any derived results of analysis may be an important requirement, particularly in the public sector. The pressing need for enhancement of identity, reliability, authenticity and audit services, including naming and addressing services, in this emerging environment is discussed in this paper. Some current and appropriate technologies currently being offered are also examined. However, severe limitations in addressing the problems identified are found and the paper proposes further necessary research work for the area. (Note: This paper is based on an earlier unpublished paper/presentation “Identity, Addressing, Authenticity and Audit Requirements for Trust in ERP, Analytics and Big/Open Data in a ‘Cloud’ Computing Environment: A Review and Proposal” presented to the Department of Accounting and IT, College of Management, National Chung Chen University, 20 November 2013.)

Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.

Risk-management of UAS robust autonomy for its integration into civil aviation safety frameworks

This paper discusses a model of the civil aviation reg- ulation framework and shows how the current assess- ment of reliability and risk for piloted aircraft has limited applicability for Unmanned Aircraft Systems (UAS) with high levels of autonomous decision mak- ing. Then, a new framework for risk management of robust autonomy is proposed, which arises from combining quantified measures of risk with normative decision making. The term Robust Autonomy de- scribes the ability of an autonomous system to either continue or abort its operation whilst not breaching a minimum level of acceptable safety in the presence of anomalous conditions. The decision making associ- ated with risk management requires quantifying prob- abilities associated with the measures of risk and also consequences of outcomes related to the behaviour of autonomy. The probabilities are computed from an assessment under both nominal and anomalous sce- narios described by faults, which can be associated with the aircraft’s actuators, sensors, communication link, changes in dynamics, and the presence of other aircraft in the operational space. The consequences of outcomes are characterised by a loss function which rewards the certification decision

Management of project knowledge at various maturity levels in PMO, a theoretical framework

Management of project knowledge is a critical factor for project success. Project Management Office (PMO) is a unit within organisations to centrally facilitate, manage and control organisational project for improving the rate of project success. Due to increasing interest of developing PMO, the Project Management Maturity Model (PMMM) has been proposed to develop PMOs gradually. The PMMM contributes to evolvement of PMO from immature to mature level through addressing appropriate PM practices. Despite the importance of project knowledge, it has not been extensively investigated in project environments. In addition, the existing PMMMs not only do not address management of project knowledge, but also they recommend little criteria to assess the maturity of PMO from KM point of view. The absence of KM discussion in current PMMMs was defined as the subject of a research project in order for addressing KM practices at various maturity levels of PMO. In order to address the mentioned gap, a framework has been developed based on the current discussions of both PM and KM. The proposed framework comprises three premises: KM processes and practices, PMMM, and KM Maturity Model (KMMM). The incorporation of KMMM practices at various maturity levels of PMO is one of the significance of this framework. It proposes numbers of KM strategies, processes, and practices to address project knowledge management at various levels PMO. This framework shall be useful guidance for developing PMOs from KM perspective. In other words, it contributes to management of project knowledge, as a key for project success. The proposed framework follows the process-based approach and it could be employed alongside the current PMMMs for PMO development. This paper presents the developed framework, theoretical background, premises, proposed KM practices, and processes to be employed in Project-based Organisations and PMOs. This framework has been examined at numbers of case studies with different maturity levels. The case studies outcomes, which will be subjects for future papers, have not shown any significant contradiction yet, however, more investigations are being conducted to validate the proposed framework.