Asset Condition Research Articles

Penyusunan Laporan Keuangan Berdasarkan SAK EMKM Pada Droplets Workshop

Micro, Small, and Medium Enterprises (MSMEs) have a big role in improving the Indonesian economy. Droplets Workshop is a micro-business referring to government regulation number 7 of 2021 which produces leather products such as bracelets, wallets, keychains, and other leather products. However, Droplets Workshop has not been able to compile financial statements due to a lack of knowledge and skills in preparing financial statements, even though financial statements can be used as material for evaluating performance, knowing asset conditions, business development, tax reporting, and as a condition for obtaining funding from financial institutions. Therefore, this study aims to prepare financial statements based on SAK EMKM at Droplets Workshop. The research method used is descriptive qualitative and data collection techniques which are interviews, documentation, observation, and literature study. The final result of this research is the required financial statements in SAK EMKM in the form of income statements, statements of financial position, and notes to financial statements. It is also expected to be a consideration for companies to carry out recording and bookkeeping according to standards.

Investigating the Effects of Climate Change on Material Properties and Structural Performance

One major issue when considering the effects of climate change is to understand, qualify and quantify how the changing climate will likely impact infrastructure assets and services as it strongly depends on current and future climate variability, location, asset design life, function and condition. Expected changes to local climatic conditions may potentially lead to changes in the degradation processes of building materials, affecting the durability and service life of structures. A key question is how climate change may produce changes of vulnerability owing to physical and chemical actions affecting structural durability or changes to exposure in terms of the intensity/frequency of extreme weather events. In this context, this article focuses on the impact on structural resistance and associated challenges, considering some recent activities of members of IABSE TG6.1. Several case studies of infrastructure assets worldwide are presented and discussed in this article.

Application of Unmanned Aerial Vehicles for Monitoring Airport Asset Surfaces

Airports facilitate the fastest mode of transportation and connect local communities and businesses with national and international destinations. The American Society of Civil Engineers (ASCE) 2021 infrastructure report card rated the aviation infrastructure category with a D+. This highlights the need for frequent monitoring and performing timely preservation techniques to ensure the optimal performance of the asset. The Texas Department of Transportation (TxDOT) periodically inspects the regional airports in Texas through visual surveys and estimates the pavement condition index (PCI) for each airport on a network scale. These ratings are used to assess the need for rehabilitation in a timely manner. In this study, an attempt was made to use an unmanned aerial vehicle (UAV) mounted with an optical camera to inspect and evaluate the condition of various airport assets. Several observations were outlined to conduct a safe inspection of airport assets using UAVs. A comparison of PCI values, grouped into three categories, obtained from traditional and aerial inspections was made to understand the feasibility of using this new technology for airport asset management. It was observed that both inspections classified most of the airport assets similarly. The traditional inspection was observed to be quicker as it requires inspection of only sampled units, however, UAV data processing takes a relatively long time to offer a comprehensive digital footprint and immersive visualization experience of the whole airport assets. Overall, UAVs are identified to have a great potential as a data collection tool supplementary to the current traditional practices.

Improving Data-Driven Infrastructure Degradation Forecast Skill with Stepwise Asset Condition Prediction Models

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) Nearest Neighbor. Model performance was evaluated against BUILDER SMS, the industry-standard asset management database, using data for five roof types on 8549 facilities across 61 U.S. military bases within the United States. The stepwise Weighted Slope model more accurately predicted asset degradation 92% of the time, as compared to the industry standard’s continuous self-correcting prediction model. These results suggest that using historical condition data, alongside or in-place of manufacturer expected service life, may increase the accuracy of degradation and failure prediction models. Additionally, as data quantity increases over time, the models presented are expected to improve prediction skills. The resulting improvements in forecasting enable decision makers to manage facility assets more proactively and achieve better returns on facility investments.

Investigating the Effects of Climate Change on Structural Actions

The changing climate with resulting more extreme weather events will likely impact infrastructure assets and services. This phenomenon can present direct threats to the assets as well as significant indirect effects for those relying on the services those assets deliver. Such threats are path-dependent and place-specific, as they strongly depend on current and future climate variability, location, asset design life, function and condition. One key question is how climate change is likely to increase both the probability and magnitude of extreme weather events under different scenarios of climate change. To address this issue, this paper investigates selected effects of climate change and their consequences on structural performance, in the context of evolving loading scenarios in three different continental regions: Europe, North America, and Asia. The aim is to investigate some main place-specific changes of the exposure in terms of intensity/frequency of extreme events as well as the associated challenges, considering some recent activities of members of the IABSE TG6.1. Climate change can significantly affect built infrastructure and the society by increasing the occurrence and magnitude of extreme events and increasing potential losses. Therefore, specific relationships relating hazard levels and structural vulnerability to climate change effects should be determined.

Financial credit risk assessment of online supply chain in construction industry with a hybrid model chain

Under the influence of COVID-19, although upstream small- and medium-sized enterprises (SMEs) in construction industry chain suffer from high operating costs and tight cash flow problems, their financing demands are even stronger. As an electronic and platform-based comprehensive service, online supply chain finance can ease the financing problems of SMEs in the construction industry. However, it has become an important issue for financial institutions to effectively assess the credit risk in the process of online supply chain financing. In this paper, an online supply chain risk assessment method, which is based on a hybrid model chain, including eXtreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling TEchnique for Nominal and Continuous (SMOTENC), and Random Forest (RF), is proposed to identify and control the credit risk of financial institutions. Specifically, we establish the financial credit risk assessment system with respect to the characteristics of financing enterprises in the construction industry supply chain, including the status of financing enterprises, the status of core enterprises, the operating status of the supply chain, and the status of assets under financing. On the basis of the system, the best index number of the assessment system and the minority samples are obtained by the XGBoost algorithm and the SMOTENC algorithm, respectively. The classification method based on RF is applied to judge the credit risk of financing enterprises in the supply chain of construction industry. In the simulation stage, we take upstream SMEs in the supply chain of construction industry in China as an example for empirical analysis to validate the effectiveness of our proposed method. The credit risk assessment method proposed in this paper has better performance than the commonly used ones in the academic field with an average improvement on assessment accuracy for 6.39% and an average increase of Area Under Curve for 6.95%. Our study provides meaningful exploration on the fund monitoring system of the financing service platform to improve financing efficiency and risk management level.

Conservation and Enhancement of the Pietrabbondante Archaeological Site between History, Geology and Emerging Crowd-Based Digital Technologies

Attention to the condition assessment and conservation of cultural heritage is increasing due to growing sensitivity to the preservation of the built and natural environment and awareness of the risks associated with natural hazards. In this context, a comprehensive approach to the conservation and valorization of cultural heritage requires the combined action of different skills to achieve reliable assessment of the conditions of valuable assets and sites based on qualitative and quantitative indicators. This paper explores the issues of conservation and sustainable management of archaeological sites, considering humanistic and technical aspects. It reviews the current rules and practices concerning the maintenance and administration of archaeological heritage and outlines the implementation of a novel procedure, based on low-cost tools, to assess and describe the current condition of archaeological assets. Specific attention is paid to the interaction between experts and researchers operating in both humanistic and technical fields and to the knowledge and data contribution available on modern web-based platforms to implement reliable and low-cost data acquisition for the development of worthwhile plans for the conservation and enhancement of cultural heritage. The Samnite complex of Pietrabbondante, Molise, South Italy, is employed to apply and check the proposed tools in the real world.

Understanding knowledge needs for Scotland to become a resilient Hydro Nation: Water stakeholder perspectives

Freshwater resources provide ecosystem services that support human prosperity and development. Future changes in climate, land-use, and population could lead to detrimental impacts on freshwater quality and quantity, threatening drinking water, irrigation and energy production. To increase resilience there is the need to better understand the possible impacts of future changes on freshwaters. We use Scotland – and its legislative agenda ‘Scotland: The Hydro Nation’ – to understand the knowledge needs of water stakeholders as a first step in building freshwater resilience. By interviewing water stakeholders across Scotland, we found an overarching ‘knowledge need’ for a greater understanding of the cumulative impacts on freshwaters associated with the interactions between multiple climatic and socio-economic drivers and their associated pressures. Stakeholders also identified five specific ‘knowledge need’ themes; large-scale land-use change, future water demands, water value, integrated development planning and water asset conditions. We identify a willingness of stakeholders to participate in systems-thinking approaches to address these ‘knowledge need’ themes. The ‘knowledge needs’ can be used to inform research priorities to support water-related policy in Scotland. We propose that participatory methods should be applied following the Driver Pressure State Impact Response (DPSIR) framework to address ‘knowledge need themes’ to identify and appraise adaptive management and policy options. Our methods used to identify stakeholder knowledge needs are a time, cost and environmentally effective way for collecting rich data. Methods can be replicated in other regions and water policy contexts to understand where increased knowledge is required to help build future resilience.

Village‐level livelihood security: A case study on a wasteland‐dominated forest fringe region of rural India

AbstractThe majority of the Indian population lives in rural areas, and the livelihood security of a wasteland‐dominated and forest‐fringe region in rural India is mostly dependent on primary economic activities. Under this backdrop, understanding the spatial pattern of livelihood security is essential for reducing poverty (Sustainable Development Goal 01) and eradicating spatial inequality in socio‐economic conditions (SDG 10). The study focuses on estimating the magnitude of livelihood security by combining the availability of natural resources, housing, drinking water, and sanitation facilities, economic condition, socio‐cultural condition, and availability of assets using the Analytical Hierarchical Process (AHP) at the village level. The villages situated along the riverside (Shilabati River) of the north‐eastern part revealed a higher degree of livelihood security, whereas forest‐ and wasteland‐dominated central and south‐western parts exhibited a lower level of livelihood security. This demarcation of villages or rural units may help policymakers to frame adequate, sustainable, inclusive micro‐level policies, enhancing socio‐economic sustainability and livelihood security as well as reducing spatial inequality.

Concurrent 18. Presentation for: Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

Presented on Wednesday 18 May: Session 18 The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan. To access the presentation click the link on the right. To read the full paper click here

Concurrent 10. Presentation for: Unlocking Gippsland reserves with a minimum kit solution – West Barracouta

Presented on Wednesday 18 May: Session 10 The West Barracouta (BTW), field one of the largest proven undeveloped gas fields off the south-eastern Australian coast, commenced production in April 2021. The project was developed by Esso-BHP Gippsland Basin Joint Venture (JV), a 50–50 JV between ExxonMobil’s subsidiary Esso Australia and BHP Petroleum. The BTW gas field is situated ~6 km south west of the existing Barracouta gas field and platform, the first Gippsland Basin Joint Venture platform in Bass Strait. The BTW development involved drilling two subsea production wells, installation of umbilical controls from the existing platform and installation of a ~6 km subsea flowline to connect to existing gas pipeline via a hot tap. The BTW team will share insights on how it: (1) utilised minimum kit solution and verification of existing asset condition; subsea hot tap into operational gas pipeline and re-use of existing platform J-tube to minimise works on and around the Barracouta Platform (including diving work). (2) Managed an Operator first – co-mingled wet gas subsea pipelines while operating in hydrate regions; via detailed water detection metre/software solution, downhole zone isolation and systematic operating guidelines. (3) Leveraged Installation Contractor competitive Front End Engineering and Design (FEED) enabling optimisation of facilities design/execution strategies to align with strengths and maximise overall project value. (4) Leveraged the OneSubsea/Subsea 7 Subsea Integration Alliance and global worldwide network to execute the subsea design (including qualification), manufacture and installation. (5) Utilised the incumbent Engineering, Procurement and Construction (EPC) contractor to integrate the subsea and topsides control systems to support a single operation system linked to the existing to Distributed Control System (DCS). To access the presentation click the link on the right. To read the full paper click here

Generation of in-Group Asset Condition Data for Power System Reliability Studies

In a power system, unlike some critical and standalone assets that are equipped with condition monitoring devices, the conditions of most regular in-group assets are acquired through periodic inspection work. Due to their large quantities, significant amount of manual inspection effort and sometimes data management issues, it is not uncommon to see the asset condition data in a target study area becomes unavailable or incomplete. Lack of asset condition data undermines the reliability assessment work. This paper tackles this important problem from an unconventional way it explores how to generate numerical and non-numerical asset condition data based on condition degradation, condition correlation and categorical distribution models. Empirical knowledge from human experts can also be incorporated in the modeling process. Also, a probabilistic diversification step can be taken to make the generated numerical condition data probabilistic. This method can generate close-to-real asset condition data and has been validated systematically based on two public datasets. An area reliability assessment example based on cables is given to demonstrate the usefulness of this method and its generated data. This method can also be used to conveniently generate hypothetical asset condition data for research purposes.

Ageing assets at major hazard chemical sites – The Dutch experience

Operators of major hazard chemical sites increasingly face the problem that their assets are ageing. This paper highlights the major hazard aspects of ageing, particularly in the Netherlands where there are around 400 major hazard chemical sites, many coming to the end of or exceeding their design lifetime. Targeted inspections find failures in the safety management of ageing and every year there are ageing related accidents. Dutch investigated major accidents are analysed in a bow-tie structured database called Storybuilder, which contains 83 major accidents resulting from material degradation, this being 25% of all accidents in the database. The paper provides unique details on the accident causes and the difficulties operators have in foreseeing the outcomes. It explains the reasons for taking a holistic approach to modelling, which considers management, human and technical aspects. The analysis results provide information on the safety barrier, barrier task and barrier management failures. A detailed ageing accident scenario is also illustrated within the holistic model. Two prevention approaches are suggested. One uses scenarios as a basis for identifying the necessary prevention measures. The other results from a Dutch multi-disciplinary maintenance programme concerning smart maintenance, a shared initiative approach of companies looking for innovative solutions. The conclusion is that a scenario-based approach is needed for identifying currently unanticipated material degradation causal events and that, given the condition of ageing assets, a shift to condition-based maintenance, combined with technical, organisational and cultural changes, underpins the future approach to physical ageing.

Minimising pipeline leaks and maximising operational life by application of machine learning at Cooper Basin

The development of technologies in the last few decades has enabled operators to collect significantly more data than previously possible. Despite availability, making data-driven decisions on asset health, and developing efficient asset management strategies, is not common. This is mainly due to challenges with compilation, and alignment of all the data into a comprehensive picture of pipeline integrity, as it consumes significant resources deploying conventional methods. A critical advantage of modern data storage, analysis and visualisation techniques is the relative ease of performing statistical assessments of integrity data. Analysis of correlated data can be equally challenging as algorithms used can be overly simplistic and inaccurate. Machine learning algorithms parse, analyse and learn from data, enabling the operators to make an educated decision. This has been extensively deployed in other industries such as finance, healthcare and supply chain management but has never been fully developed and enhanced in pipeline integrity industry until very recently. This paper provides an overview of the development in machine learning tools in pipeline integrity, allowing enhancement of asset performance, through the application of machine learning and automation, to predict integrity threats, and prevent leaks and failures. It provides a case study where a tool was developed, and this technique was successfully implemented across a significant number of upstream pipelines in the Cooper Basin, enabling the Santos integrity engineering team to make the most effective decisions on asset condition and to develop a data-driven asset management plan.

Unlocking Gippsland reserves with a minimum kit solution – West Barracouta

The West Barracouta (BTW), field one of the largest proven undeveloped gas fields off the south-eastern Australian coast, commenced production in April 2021. The project was developed by Esso-BHP Gippsland Basin Joint Venture (JV), a 50–50 JV between ExxonMobil’s subsidiary Esso Australia and BHP Petroleum. The BTW gas field is situated ~6 km south west of the existing Barracouta gas field and platform, the first Gippsland Basin Joint Venture platform in Bass Strait. The BTW development involved drilling two subsea production wells, installation of umbilical controls from the existing platform and installation of a ~6 km subsea flowline to connect to existing gas pipeline via a hot tap. The BTW team will share insights on how it: (1) utilised minimum kit solution and verification of existing asset condition; subsea hot tap into operational gas pipeline and re-use of existing platform J-tube to minimise works on and around the Barracouta Platform (including diving work). (2) Managed an Operator first – co-mingled wet gas subsea pipelines while operating in hydrate regions; via detailed water detection metre/software solution, downhole zone isolation and systematic operating guidelines. (3) Leveraged Installation Contractor competitive Front End Engineering and Design (FEED) enabling optimisation of facilities design/execution strategies to align with strengths and maximise overall project value. (4) Leveraged the OneSubsea/Subsea 7 Subsea Integration Alliance and global worldwide network to execute the subsea design (including qualification), manufacture and installation. (5) Utilised the incumbent Engineering, Procurement and Construction (EPC) contractor to integrate the subsea and topsides control systems to support a single operation system linked to the existing to Distributed Control System (DCS).

Meeting decision support requirements through natural capital accounts: a case study in England’s National Nature Reserves

ABSTRACT Natural Capital Accounts extend traditional balance sheets to include valuation of non-market environmental benefits and asset values. They aim to bring the often-overlooked value of nature into decision-making, thus providing an effective decision-support tool. We compare Natural Capital Accounts to UK decision-support guidance, which leads us to three innovations that we apply to a case study of the National Nature Reserves in England. First, we use a reporting format, which explicitly reports gaps in quantification and valuation. Second, we provide ‘traffic-light’, confidence level information for our value results. Third, we report on the ecological state of assets and incorporate this evidence into the headline results. These innovations address common weaknesses whereby partial monetary values may mislead decision-makers and confidence levels are highly variable, yet often ignored. Information on the ecological state of assets is often only partial, non-systematic and not presented with the summary results. Producing this account required ecological and economic evidence to be treated as equally important. These three innovations produce an account that is transparent and provides a useful snapshot of the condition of natural capital assets.

Study of irrigation performance index in Saba irrigation area

Saba Irrigation Area is located in Gerokgak and Seririt District, Buleleng regency, Bali. The assessment of the irrigation performance index is required to maintain the value of irrigation asset function and condition. Some problems occur in the irrigation area from the standard area of 1548 ha, but the productive land is around 889 ha or about 57% of the standard area. The problems were found in the irrigation canal, which hampered the performance of irrigation downstream, where water cannot flow to rice fields. The wet cross-section of the channel is covered by sediment. Even at several locations, the sediment height has the same elevation as the surrounding surface. The purpose of this study is to obtain value in the form of an assessment index on the primary and secondary irrigation systems, which will be classified into their respective components. Furthermore, rehabilitation priority is determined using the decision-making method with the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). The assessment of irrigation performance index scored 62.94% consisting of physical infrastructure 29.28%; crop productivity 7.24%; supporting facilities 4.95%; personnel organization 10.75%; documentation 3.40%; and water user associations 7.33%. Determining the rehabilitation priority using the AHP method and ANP method showed the same results, i.e. physical infrastructure in rank 1, personnel organization in rank 2, crop productivity in rank 3, water user associations in rank 4, supporting facilities in rank 5, and documentation in rank 6. Determination of the selected rehabilitation in the physical infrastructure aspect is the sub-aspect of irrigation channels. Irrigation channels get the highest score with the worst damage conditions from all sub-aspects.

Infrastructure Asset Management: Historic and Future Perspective for Tools, Risk Assessment, and Digitalization for Competence Building

This article aims at analyzing the historic development of infrastructure asset management (IAM) resulting from the increase of challenges over time. Furthermore, it aims at suggesting the corresponding requirements for the enrichment of educational programs to provide the decision makers of tomorrow with the right competences. The evolution of IAM is here described as characterized by three periods introducing an increased complexity of analysis and thereby, a more powerful system for urban water management: (a) Data collection and development of computerized information systems including statistical methods for information management; (b) application of risk analysis including sources of hazards and their consequences; and (c) introduction of a holistic sustainable perspective including governance, social and economic aspects (circular economy), environmental impacts, and the condition of physical assets including digital systems. A variety of competencies are needed to obtain the safe management of urban water systems, in particular for the provision of water services in medium- and large-scale cities. Similar competencies are needed for other infrastructures, like buildings, roads and railroads, and IT systems. The elements of sustainability including risk assessment and digitalization should be incorporated in master programs for civil engineering world-wide. This paper is not designed as a scientific paper, but as inspirational for IAM practitioners and for the development of enriched educational programs of technical universities.

After-Sales Service Contracting: Condition Monitoring and Data Ownership

Problem definition: Condition monitoring (CM) of durable assets, whereby sensors continuously monitor the health of an asset, is heralded as a key application of the Internet of Things. However, questions about ownership of the sensor data are seen as a key barrier to adoption. We model an after-sales supply chain in which the asset manufacturer provides maintenance and repair services to a customer that operates the asset. The asset condition deteriorates in a stochastic fashion and will eventually fail if not repaired. Methodology/results: We analyze a performance-based contracting problem considering manufacturer maintenance effort (the condition at which preventive maintenance is performed) and customer operating effort (which reduces the rate of condition deterioration). With information asymmetry on the customer’s effort cost, we analyze this contracting problem in a principal-agent model with double moral hazard. In the centralized setting, we establish that the benefit of CM increases and then decreases in the asset’s deterioration rate and that CM may increase or decrease the benefit of customer effort depending on the deterioration rate. In the decentralized setting, we prove that CM always benefits the manufacturer and the supply chain, but it may hurt the customer if the asset reliability is sufficiently high. Managerial implications: These results have important implications for the effect of sensor-data ownership. The manufacturer will adopt CM if it owns the data, but the customer may block CM adoption if it owns the data. We show that this CM adoption barrier can be overcome by the manufacturer offering to pay an appropriate data access fee. However, under this arrangement, the manufacturer may not benefit from a more-effective customer operating effort. We discuss the resulting implication on the manufacturer’s product design and the business model between selling and leasing.

An approach for Building Defects Detection using VGG16

Customers are increasingly looking for fast and efficient ways to frequently check and inspect the condition of their buildings so essential repairs and maintenance can be done in a timely manner. Act quickly before they become too dangerous and costly. Traditional methods for this type of work commonly comprise engaging inspection officers to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the building defects, including cost estimates of immediate and project the long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time-consuming and expensive and pose health and safety threats to inspection officers, particularly at height and roof levels which are difficult to access. This project aims toward an automated detection and localization of key building defects, e.g., moss, cracks, deterioration and stains, from images using Convolution Neural Networks. The proposed model was built on a pre-trained CNN classifier of VGG-16, with class activation mapping (CAM) using a squeeze net for object localization. The challenges and limitations of the model in real-life applications have been identified. The proposed neural network model has proved that it can accurately detect and localize building defects. Key Words:Object Detection, Convolutional Neural Network, Deep Learning, VGG16