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Maturity models (MMs) have witnessed exponential increase due to their successful application in several domains. However, there is an absence of review that guides researchers in developing, applying and validating Public-Private Partnership maturity models (PPPMMs). This study examines PPPMMs, provides guidance on the topic and highlights gaps in the literature. A literature search on selected electronic databases was conducted, and the study adopted the widely accepted Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA). The study identified a total of four thousand six hundred and eighteen (4,618) studies, and twenty-one studies (21Nr) were rigorously selected. The results revealed PPPMMs as an emerging area of research with a low number – 21 publications since its deployment for about two (2) decades. Similarly, the findings unveiled a lack of uniformity in conceptualising the terms, dimensions used, and methodology adopted. This finding is attributed mainly to the limited use of the theoretical lens, which considerably weakens the model’s theoretical foundation and limits its potential to guide improvement. Additionally, there are more efforts in developing MMs than applying and validating them. Furthermore, there is an unbalanced focus on descriptive models over prescriptive and comparative models, which inhibit the model’s potential to guide improvement. Future work should provide a solid ground to the field using a theoretical lens and focus on prescriptive models with a strong emphasis on application and validation. This research is the first of its kind that synthesises and brings together available PPPMMs literature into one place. It also contributes to the body of knowledge by highlighting areas of research that require immediate attention to enhance the much-needed success of PPP in the post-COVID-19 era.

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

An Autonomous Structural Health Monitoring (SHM) System for Fixed Offshore Structures is a tool used to monitor the state or the health of a structure in terms of its integrity and strength in an automated manner. An SHM system framework comprises of software and hardware integration equipped with IoT capability to collect raw data, online data transmittal to onshore, a back-end engine to process data into useful engineering information and display the monitoring results through engineering parameters and digital twinning, which emulates the real condition of the structure offshore. The prominent monitoring method for a structure's strength is through global monitoring, using structural modal properties as the measuring parameter to indentify a certain structure's global integrity, specifically using its natural frequency. This paper aims to layout the framework of an autonomous SHM system for global monitoring which is implemented onto a seismically vulnerable fixed offshore structure.

In the study of structural strength, the reserve strength ratio provides a measure of the ultimate strength capacity of a structure. Under actual site conditions, the reserve strength ratio may vary from its design values with loss of stiffness and changes in structural integrity. Changes in the vibrational response of a structure due to loss of stiffness is observed as a form of structural health monitoring (SHM). The aim of this study is to investigate the relationship and sensitivity of the reserve strength ratio of a structure to changes in natural frequency due to damage occurrences as a measure of global structural integrity. The reduction of stiffness is simulated by the sequential removal of members according loading path within the model. To obtain the values used for comparison, a non-linear pushover analysis and eigenvalue analysis is utilized to obtain the Reserve Strength Ratio (RSR) and eigenvalue for intact as well as simulated progressive damage conditions. The pattern recognized from the analysis performed indicated that as the reserve strength ratio (RSR) is reduced with reduction of stiffness by the removal of primary and secondary members, the eigenvalues for each respective model showing similar reductions.

In Malaysia, over 400 oil and gas platforms are in operation. In every life cycle of a platform, it undergoes decommissioning. The most used method is by relocating the asset to an onshore fabrication facility. The alternative to onshore decommissioning is by reefing structures in-situ or ex-situ basis. to achieve the environmental assurance of a successful decommissioning by reefing, two domains of parameters were overviewed. the study involved hydrodynamic simulation of platforms. The first domain is in relations to the site to determine its suitability against hosting marine life and thriving coral formulation (Site Suitability). the other domain is in relations to the structure's physical and hydrodynamic properties (structural viability). Site suitability is governed by water depth and current velocity. Structural viability is governed by two sub-domains, one is where dimensions and complexity of the structure are assessed using dimensionality analysis. And the other is its hydrodynamic properties (Vortex Shedding Frequency, Pressure, Wake Region indices).

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered.

This study presents an experimental investigation on the effect of quartz powder on the compressive strength of high performance engineered cementitious composites (HP-ECC). Four different HP-ECC mixtures were considered ECC mixture with PVA fibres and zero quartz powder, ECC made with PVA fibres and quartz powder, ECC made with steel fibres and zero quartz powder, ECC made with steel fibres and quartz powder respectively. Different percentages of steel and PVA fibres were investigated at 0 to 2%. The compressive strength of HP-ECC was examined for 1, 7, 14, and 28 days. Based on the experimental results, it was found that the inclusion of quartz powder leads to enhancement in the compressive strength. Compressive strength of more than 100 MPa was obtained for both ECC made with quartz powder. On the other hand, the fibres type has a negligible effect on the performance of HP-ECC.

Dynamic load acting on the side-by-side mooring system is one of the important factors to estimate the offloading operability of the FPSO. In this paper, a numerical mooring analysis for the side-by-side moored FPSO-tanker system is investigated. Floating Structure analysis is divided into two parts. The first is to calculate the hydrodynamic coefficient, and the second part is calculating the system responses in the time-domain analysis. The prediction of FPSO-Tanker responses is necessary during offloading. This research presents the possibility of the use of Artificial Neural Networks model for the prediction of the responses of FPSO-Tanker system with spread-mooring configuration. The ANN model considering various metocean data such as azimuth angles, significant wave height, wind load, swell, hawser and the mooring configurations. The results of this study indicated that the ANN model shows an accurate prediction of the FPSO-tanker response that are required for the design of side-by-side vessels during offloading.

In relation to the use of retrofit materials on damaged constructions, application on earthquake-resistant buildings, and for the strengthening and rehabilitation on weakened regions, there is a need for a more superior material than concrete. Application sites include beam-column joints, corbels, link-slabs, deep beams, support regions and dapped-end areas. Fiber reinforced engineered cementitious composites (FR-ECC) can address this issue, because FR-ECC is one of the composite materials that has high strength, ductility and durability. In order to develop FR-ECC, this study was done to investigate the effect of adding quartz powder on the compressive strength capacity and properties of FR-ECC through the use of polyvinyl alcohol (PVA) and steel fibers. The volume fraction of fiber was set to 0%–2%. To support the friendly environment, FR-ECC uses by-product materials such as fly ash and silica fume, with a cement content less than 600 kg/m3. In terms of the experimental investigation on FR-ECC, this work conducted the fresh property tests showing that PVA fibers have quite an influence on ECC workability, due to their hydrophilic behavior. By adjusting the superplasticizer (SP) content, the consistency and high workability of the ECC mixes have been achieved and maintained. The test results indicated that the PVA and steel fibers-based ECC mixes can be classified as self-compacting composites and high early compressive strength composites. Significantly, addition of quartz powder into the ECC mixes increased the compressive strength ratio of the ECC samples up to 1.0747. Furthermore, the steel fiber-based ECC samples exhibited greater compressive strength than the PVA fibers-based ECC samples with the strength ratio of 1.1760. Due to effect of the pozzolanic reaction, the fibers dispersion and orientation in the fresh ECC mixes, so that the cementitious matrices provided the high strength on the FR-ECC samples. During the compression loading, the bulging effect always occurred before the failures of the fibers-based ECC samples. No spalling occurred at the time of rupture and the collapse occurred slowly. Thus, FR-ECC has provided unique characteristics, which will reduce the high cost of maintenance.

Offshore structures are susceptible to damages due to hurricane. The main subject of this research, free standing drilling riser (FSDR), is an innovation to the drilling riser that enables rapid disconnection prior to hurricane conditions. This allows the offshore drilling rig and operator to move away from hurricane path prior to hurricane landing. Yet, currently, the emergency disconnection decision of FSDR is decided by a rig manager. Past incidents have shown that human error can cause catastrophic damage. Studies on FSDR operations under hurricane condition are scarcely available. The most relevant studies, free standing riser (FSR) behaves differently. With the increase in hurricane intensity over the past 50 years, this increases the risk in FSDR operations. In this work, the hydrodynamic response of FSDR under hurricane conditions was investigated. Four simulation studies were conducted in Orcaflex finite element modeling (FEM) software. These studies were conducted to determine the effect of emergency disconnection location, the optimum emergency disconnection location, the effect of various Saffir Simpson maximum significant wave height and hurricane generated swell on the hydrodynamic response of FSDR. Hurricane generated wave spectrum was used to replicate hurricane conditions. The simulation study shows that the lateral displacement of FSDR decreases in a 3rd order polynomial function with the emergency disconnection location, LD = −6e−6d3 + 0.0024d2 − 0.3348d + 19.179. In addition, the optimum emergency disconnection location along drilling riser line is at 87.50% of the original water depth. The lateral displacement of FSDR increases exponentially with Saffir Simpson scale storm intensity, y = 1.7537e0.0386x. Lastly, the stronger the swell component of hurricane generated swell, the higher the hydrodynamic responses. Wind wave component of hurricane generated swell did not influence FSDR as the riser was in the free standing mode. The limitation of this research is the findings are applicable for this simulation setup only. Further validations are required with experimental setup and various types of offshore structure.

In offshore engineering, the design of effective marine platforms compatible with the offshore environmental condition is a challenging task, especially accurate estimation of hydrodynamic coefficients that affect the viscous forces. Practically, the well-known Morison’s equation is often adopted by designers to calculate wave loads on slender structures, where the values of drag and inertia coefficients are selected as constant values based on the design code of practice’s recommendations. However, the mass and drag coefficients must be determined empirically based on specific met-ocean data for the operation location. Thus, the objective of this study is to evaluate the Hydrodynamic Forces on Circular Cylinders fitted with sacrificial anodes using empirical methods, and validate the results using Computational Fluid Dynamic (CFD). It is found that the major parameters that affect the drag and inertia coefficients are the water depth, wave heights, wave frequencies, the pipe diameters, and the presence of anode fittings.

The paradigm change in web structure has turned the world of information systems and data sharing to be a huge and diverse storage. As many oil and gas companies vie for attention due to the downturn, more data management is needed. Therefore, oil and gas industries are hungry to share their information for competitive advantage. The challenge is the redundancy or/and unorganized information present in large amounts of data. This paper proposed a methodology for the development of semantic big data for oil and gas data exchange that facilitates data integration. It provides a framework that will be used for accessing the oil and gas data; and integrate the oil and gas unstructured, semi-structured and structured data for better accessibility to users.

This study seeks to measure the temperature difference of 3 types of interlocking bricks and 2 types of conventional bricks, all which are available in Malaysian market. The study aims to assist Malaysia’s residential construction dilemma in masonry. This is done by constructing 5 models using different types of bricks with the same dimensions, finishes and placed in the same area. Six points were taken to measure the temperature differences. The equipment used are 6 Thermocouples and HOBO UX120-014M data logger. Intervals of 15 minutes were recorded with duration of four months in Tronoh, Perak. Results show the best performed brick is IB2, even though IB1 and Clay bricks depict significant temperature differences. Comparing the temperature differences of IB1 and Clay brick, IB1 provide a better performance in a hot weather due to its higher thermal conductivity.

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.

This paper describes the Linked Data initiative in meteorological environment. MetOcean data are meteorological and oceanographic datasets that provide information to meteorological industries, petroleum industries as well as research institutes. The MetOcean has a myriad of data and scattered along the globe which lack Linked Data or Semantic Web capabilities. Linked Data is a concept of publishing interoperable data globally. As a way of integrating MetOcean data, this work used D2R server, a server that manages data sources and translates it to Web data. Therefore, the paper elucidates the process of developing Linked Data in Meteorological sector. Also, the results were queried using SPARQL endpoint as a graph. This approach was analyzed using F-measure which reveals the system is sufficient for querying and accessing the data as a graph.

This paper suggests a Resource Description Framework (RDF) model for effective handling of distributed data relating to the Semantic Web. RDF is a data model that integrates aptly structured data. The World Wide Web is facing great challenges in data retrieval as a result of increase and request of knowledge from different sources. This brings the issue of information overload. Therefore, in minimizing this challenge, the paper presents the process of modeling RDF knowledge base which is the essential step of Semantic Web development. As a state-of-the-art research, a meteorological and oceanographic dataset has been used to provide the basic concepts of RDF serialization. The result accessed through the developed SPARQL endpoint. Thus, it implies that the approach is sufficient for querying as well as data representation of distributed data.

This paper suggests an RDF Model for effective management of distributed data on the Semantic Web. Resource Description Framework (RDF) is a data model that unifies aptly structured data. The Web is facing great challenge in data retrieval due to the increase and request of information from different sources. This brings the problem of information overload. In minimizing this problem, we provide a knowledge model for structuring data which is the first step of building Semantic Web. In particular, to elucidate the model, Meteorological and Oceanographic (MetOcean) data have been used. We present the result using SPARQL query method. Thus, the finding shows that the model is ideal for querying and data representation of distributed data.

Semantic Web provides precise, timely, searchable and well-define information across different sources of data. However, there is need of solving the problem of interoperability due to everyday increase of data from different sources. Whereby, the resourceful information or data integration between human and machines are needed. Thus, more precisely, this paper addresses an ultimate problem of integrating and utilizing the accurate data on information systems. The proposed methodology of this paper includes: (1) grounded theory that focuses on particular interaction between human and machines, (2) developing the analysis; the research introduces the Software Development Life Cycle (SDLC) approach, from the development to implementation stages, (3) sources of data; for state-of-the-art research, we propose to use MetOcean (Meteorological and Oceanographic) data, and (4) Research instrument which is to set our research into practice where we will build Semantic Web application using Franz AllegroGraph and use SIMILE (Semantic Interoperability of Metadata and Information in Unlike Environments). Finally, we provide the Ontology model of the for the state-of-the-art research in meteorological and Oceanographic domain.

Ontology is an explicit representation of a particular domain of interest. This paper focuses on the inherent problem of intractability (data complexity) due to data capturing for heterogeneous sources. We proposed to follow the tradition of knowledge representation and reasoning processes to develop a Semantic Web. Building query systems is the ultimate goal for this study. Conjunctive queries were adopted over ontology methods to address the data complexity. The Conjunctive queries are the form of queries in first-order logic that deal with conjunction (Λ) and existential quantification (∃) in database atomic formulae. State of the art meteorological and oceanographic domain provides the motivation for this work. We found that the querying method facilitates the tractability in reasoning and logical systems.

Web 2.0 is increasingly becoming a familiar pedagogical tool in higher education, facilitating the process of teaching and learning. But this advancement in information technology has further provoked the problems like plagiarism and other academic misconduct. This paper evaluates the patterns of use and behavior of tertiary level students towards the use of Web 2.0 as an alternative and supplemental eLearning Portal. A total of 92 students' data were collected and analyzed according to [1] 'Self-Determination Theory' (SDT). It was found that students use social websites for chatting, gamming and sharing files. Facebook, YouTube and Wikipedia are ranked as the most popular websites used by college students. It also reveals that students have an inherent desire of expressing ideas and opinion online openly and independently. This sense of freedom makes students feel more competent, autonomous or participative and find learning to be less tedious. Therefore, this report, recommends educators to adopt strategies for acknowledging students' feelings and activities online to reinforce positive behavior effective learning. Finally, we discussed the implications of Web 2.0 on education.