Performance Of Infrastructure Research Articles

Impact of ICT on regional supply chains in CEECs

Purpose: This paper aims to examine the impact of information and communication technologies and their availability in the Central and Eastern European countries that are members of the EU on regional value chains and regional supply chain management and logistics in CEECs in the context of the growing e-commerce industry.Methodology: We used a balanced data panel of 11 countries over 9 years (2011-2019) to examine the impact of ICT indicators on regional supply chains. Th e analysis included the ordinal Spearman rank correlation coefficient and the panel GMM method, which accounts for endogeneity. We implemented the Arellano-Bond estimator within the GMM framework, considering the ordinal nature of the dependent variable. This approach allowed for an effective analysis of the complex relationships in the data, considering the panel structure and the individual country observations.Results: The findings indicate that the hypothesis claiming that the integration of digital technologies in supply chains in CEEs leads to statistically significant improvements in efficiency, controllability, and cost-effectiveness, cannot be rejected. Enhanced communication, collaboration, and overall logistics performance have led to increased customer satisfaction. Additionally, the study shows a positive correlation between ICT infrastructure and logistics performance, emphasizing the crucial role of digital innovations in shaping supply chain dynamics.Conclusion: Th e study supports the hypothesis that integrating digital technologies into supply chain management in Central and Eastern Europe is associated with enhanced efficiency, resilience, and economic integration through value-added export. Th ese insights offer pathways to foster economic integration in the region.

Compressive strength optimization and life cycle assessment of geopolymer concrete using machine learning techniques

Fly ash-based geopolymer concrete is studied in this research work for its compressive strength, life cycle and environmental impact assessment contribution to the construction environment. This is in line with the United Nations’ sustainable development goals SDG9 and SDG11. However, the focus of this research paper is on the sustainability of geopolymer concrete and its overall environmental impact. The metaheuristic machine learning approaches have been deployed to predict the compressive strength (CS) of the GPC based on environmental impact considerations of the concrete constituent materials, which included fly ash, sodium silicate, sodium hydroxide, fine and coarse aggregates. The metaheuristic techniques include the k-Nearest Neighbour (kNN), support vector regression (SVR), and random forest regression (RFR), where all are optimized with the particle swarm (PSO). These metaheuristic techniques have been modified for this research work with new codes to enhance innovation in terms of run time and efficiency. The results of the life cycle assessment (LCA) evaluation of the GPC mixes based on the Ecoinvent 3 available in SimaPro and Eco-indicator 99 and CML 2001 modified in the framework of ReCiPe 2016 recent development show reduced potential of environmental acidification due to increased fly ash (FA) in the GPC mixes compared to previous results. The decisive CS and LCA predictive models, RFR-PSO and SVR-PSO respectively performed optimally above 90% and better than previous models from the literature. Overall, they present an innovative metaheuristic smart technology for the prediction of the GPC infrastructure behavior and performance integrity.

Influence of climatic parameters on the probabilistic design of green roofs

Green roofs are effective tools for stormwater control in highly urbanized areas since they allow the reduction of peak runoffs and volumes discharged in sewer systems. Their design is quite standardized, except for the thickness of the growing medium layer, which is strictly related to vegetation type and rainfall regime. The paper proposes an analytical probabilistic approach that relates the climatic variables, the growing medium thickness, and the water content in the condition of fulfilled field capacity to the probability that runoff from green roofs exceeds a fixed threshold. The developed equations also consider the possibility of a reduced retention capacity due to previous rainfall events, that strongly influence the performance of these green infrastructures, especially when short dry periods and/or low evapotranspiration rates occur. This feature, neglected by the traditional design storm approach, and only partially considered by previous analytical probabilistic models, represent a great potentiality of the proposed equations that are also more user-friendly and less time-consuming than continuous simulation analysis. The focus of the paper is on the influence of climatic parameters on runoff probability. To this aim to perform the monthly analysis is fundamental, especially when there is a strong variability of the climatic parameters throughout the year.The model was tested in a case study in Milano, Italy. The application presented a good agreement between the results obtained from the proposed equations and those obtained from the continuous simulation of recorded data. The results also highlighted the importance of performing analysis on a monthly scale.

Infrastructure and Health System Performance in Africa

Empirical findings for the effect of infrastructure on health system performance and across a range of infrastructure types in Africa are not common. This is important for ascertaining the infrastructure with more influence on health systems. Findings in this regard are vital in Africa where health systems perform poorly, with fiscal challenges for public provision of health care needs. This paper examined the effect of infrastructure types on health system performance in Africa using data for 54 countries in the region and over the period 2003–2018. Health system performance is captured by population health outcome. Findings are shown using the System GMM estimation technique. The results showed a significant effect of transport and ICT in improving the length of life and reducing under-five mortality. Improvement in ICT reduced maternal deaths. An increase in all infrastructure types (transport, electricity and ICT) significantly reduced infant mortality. From the results, only ICT is associated with improvement in all population health outcome variables used in the study. Findings suggest the key role of infrastructure on health system performance, with ICT shown to have more influence on health systems than other infrastructure types. The provision and use of ICT should therefore be given top priority in the pursuit of better health system performance in Africa.

End-to-end semi-supervised deep learning model for surface crack detection of infrastructures

Surface crack detection is essential for evaluating the safety and performance of civil infrastructures, and automated inspections are beneficial in providing objective results. Deep neural network-based segmentation methods have demonstrated promising potential in this purpose. However, the majority of these methods are fully supervised, requiring extensive manual labeling at pixel level, which is a vital but time-consuming and expensive task. In this paper, we propose a novel semi-supervised learning model for crack detection. The proposed model employs a modified U-Net, which has half the parameters of the original U-Net network to detect surface cracks. Comparison using 20 epochs shows that the modified U-Net network requires only 15% training time of the traditional U-net, but improves the accuracy by 20% upwards. On this basis, the proposed model (modified U-Net) is trained based on an updated strategy. At each stage, the trained model predicts and segments the unlabeled data images. The new strategy for updating the training datasets allows the model to be trained with limited labeled image data. To evaluate the performance of the proposed method, comprehensive image datasets consisting of the DeepCrack, Crack500 datasets those open to public, and an expanded dataset containing 2068 images of concrete bridge surface crack with our independent manual labels, are used to train and test the proposed method. Results show that the proposed semi-supervised learning method achieved quite approaching accuracies to the established fully supervised models using multiple accuracy indexes, however, the requirement for the labeled data reduces to 40%.

Distributed dual-layer autonomous closed loops for self-protection of 5G/6G IoT networks from distributed denial of service attacks

Internet of Things (IoT) is a major application area of the Fifth-Generation (5G) and beyond capable of providing massive machine-type communications (mMTC) at a large scale. It enables a wide range of applications such as smart cities, smart grids, smart factories and so on. In light of the huge number of devices involved, it is prohibitive to manage the massive large-scale cyber security scenarios manually. Therefore, closed automation loops are essential to automate such management. This paper proposes a new cognitive closed loop system to offer distributed dual-layer self-protection capabilities to battle against Distributed Denial of Service (DDoS) attacks. The proposed system features the novel usage of concurrent autonomous closed-loops for the different stakeholders’ business roles: Digital Service Providers (DSPs) and Infrastructure Service Providers (ISPs) respectively, suitable to provide a multi-layer self-protection defence mechanisms across multiple administrative domains. It has been designed, implemented and experimentally validated. Empirical results have shown that there is a high potential in the collaboration between the stakeholders to achieve the common goal of self-protection of infrastructures. It makes a major difference in the performance of the whole infrastructure for detecting, analysing and mitigating the threat when the proposed distributed dual-layer loops are applied instead of a standalone loop. The system has achieved a 78.12% of effectiveness compared with a 4.73% of the standalone counterpart, for a large scale attack when stopping 256 infected devices. Also, the proposed system has achieved a response time of 18 s whereas the standalone has required 57 s, achieving an optimization of performance of 316%.

Modelling barriers to wearable technologies in Indian context: validating the moderating role of technology literacy

PurposeThe purpose of this study is to examine diverse risks and barriers that influence customers' attitude leading to their actual use of wearable devices in India. This study used technological literacy as a moderating variable to understand the relationship between barriers and attitudes toward adoption of wearable device.Design/methodology/approachA survey questionnaire was developed through focused group discussions with field experts. Data were collected through online as well as offline modes. A Google form was created and its weblink was shared with the respondents using wearable devices. Both online as well as offline modes were used for data collection. Several reminders through telephone and revisits were undertaken to approach the respondents.FindingsThe results of this study indicated that psychological risk and financial risk emerged strongest barriers of wearable technologies. This was followed by infrastructure barriers and performance risk. The strength of the relationship between technological anxiety and attitudes was lower but still significant. Surprisingly, privacy risk and social risk were not statistically significant. This study also validated the impact of technological literacy as a moderator between risks and attitudes.Originality/valueThis study contributes to the research by validating numerous risks and barriers in the adoption of wearable devices. This study not only offers a novel perspective on researching diverse barriers but also elucidates the moderating role of technological literacy which has not been covered in extant literature.

The Relationship between Home Environment and Self-Management towards Academic Performance of Private Institution Students in Kuala Lumpur

Education plays an important role in providing a better life for students, and learning starts at home, specifically since they are born. This study investigated the effect of home environment and self-management on students’ academic performance at a private institution in Kuala Lumpur using descriptive and Pearson correlation analysis. The focus of the study s to find out the relationship between economic status, family involvement, home infrastructure, parenting style, self-management, and academic performance. This study involved 145 students, and data were collected using a questionnaire containing 4 parts: demographic, home environment, self-management, and academic performance. The data were analysed using Microsoft Excel. The data revealed a positive correlation between home environment, self-management, and academic performance. The results also showed that educational institutions must focus on students’ self-management to improve their academic performance. Furthermore, there is a need to look at students’ home environment, such as parenting style, family involvement, and home infrastructure, to encourage parental support toward improving students’ academic performance.

Optimization and trade-off framework for coupled green-grey infrastructure considering environmental performance

Implementing runoff control infrastructure has been regarded as an efficacious measure in stormwater management. The issue of its cost-effectiveness is a primary concern for decision makers since it is an exorbitant investment. However, most of existed studies only concentrated on the cost-effectiveness optimization of runoff control infrastructure, especially green infrastructure, between hydrological and economic aspects, and therefore, the potential layout scenarios with high extra environmental benefits could be neglected in the traditional two-dimensional frameworks. In this study, a novel carbon dioxide equivalent-based index was quantified to represent the extra environmental benefits of runoff control infrastructure besides stormwater management and was further integrated into the assessment framework. The effectiveness of green and grey infrastructure was comprehensively evaluated and traded off between hydrological, environmental and economic aspects. The results demonstrated that grey infrastructure is a better measure than green infrastructure when only hydrological (HF index) and economic (CI index) performances were considered. Nevertheless, the environmental performance (EROI index) of green infrastructure prevails over grey infrastructure, and when optimizing green and grey infrastructure simultaneously in the three-dimensional framework considering environmental effectiveness, green infrastructure is comparable with grey infrastructure. Furthermore, an appropriate composition of coupled green-grey infrastructure is requisite, which could achieve an optimal trade-off between hydrological and environmental effectiveness. The sources of environmental benefits were also identified and analyzed from three representative preference scenarios. The findings of the study could serve as a trade-off basis between green and grey infrastructure, as well as between EROI and HF.

Critical factors influencing learning from quality failures in the construction of grid infrastructure

PurposeQuality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.Design/methodology/approachThe related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.FindingsThe SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.Practical implicationsThe findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.Originality/valueExisting research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.

Exploring Factors Affecting Transport Infrastructure Performance: Data-Driven Versus Knowledge-Driven Approaches

Transport infrastructure is a fundamental component of the whole infrastructure system and socio-economic development. However, it is still a challenge to identify factors affecting large-scale infrastructure due to the lack of high-quality data and inconsistent methods. To address this issue, this study developed a comparison of data- and knowledge-driven approaches in exploring factors affecting road infrastructure performance in Western Australia using network-level high-resolution road defects data. In data-driven analysis, an optimal parameters-based geographical detectors (OPGD) model, developed based on spatial heterogeneity, was developed to investigate the contributions of explanatory factors from a spatial data perspective. In knowledge-driven analysis, a questionnaire survey was performed through group interviews with regional road management teams to analyze potential explanatory factors. A spatial analytic hierarchy process (S-AHP) approach was implemented to quantify the contributions of factors based on the survey. Finally, the consistency and difference between data- and knowledge-driven approaches are evaluated based on contributions of factors and the predictions of defect risks across the road network. The results indicate that the contributions of factors tend to be similar in both approaches, and the spatial distributions of defect risks predicted by both approaches are highly correlated. The factors and risks analyzed using both methods in rural areas are more consistent than those in urban areas due to the complexity and uncertainty of road defects. Findings from this study are critical to understanding data- and knowledge-driven approaches in transport infrastructure management and determining reasonable approaches for decision-making.

Structural Crack Detection System using Internet of Things (IoT) for Structural Health Monitoring (SHM): A Review

Monitoring the state of civil engineering infrastructure is critical for a country’s economic development since structures with long service life and timely maintenance have lower reconstruction costs. Crack occurrence is the most important element that influences the performance and lifespan of civil infrastructures like bridges and pipelines. As a result, several fracture detection and characterization approaches have been explored and developed in the domains of Structural Health Monitoring (SHM) throughout the last few decades. The major goal of implementing the Internet of Things (IoT) paradigm is to enable the Internet-based connectivity extension of various typical SHM devices. As a result, connected devices can communicate and process data, opening new possibilities in the design of acquisition systems in various disciplines of research and engineering. The researchers have extended the application of the IoT paradigm to the SHM crack detection because of the advances, ensuring that the tests done in this framework can produce good results with promising future improvements. Thus, this paper reviews structural crack detection based IoT for SHM as reported by previous research in the literature. The strengths and limitations of current systems are discussed. This paper is aimed to serve as a reference for crack detection and characterisation researchers as well as others who are interested in SHM in general. In addition, several case studies on real structures, as well as laboratory experiments for monitoring structural crack health of civil engineering structures, are also presented.

Variability of green infrastructure performance due to climatic regimes across Sweden

In the context of increasing urbanization and global warming, there is a growing interest in the implementation of green infrastructure (GI) across different climates and regions. Identifying an appropriate GI design criteria is essential to ensure that the design is tailored to satisfy local environmental requirements. This article aims to compare the hydrological performance of GI facilities in eleven Swedish cities by isolating the effect of climatic conditions using an identical GI design configuration. Long-term simulations based on 23-years of meteorological time-series were used as inputs for the Storm Water Management Model (SWMM) with Low Impact Development (LID) controls representing two types of facilities: a biofilter cell (BC) and a green roof. (GR). Large differences in potential annual and seasonal runoff retention were found between locations, driven mainly by the extent of winter/spring season, and the distribution of precipitation patterns (for BCs) and the sequence of rainy days-dry periods and evapotranspiration rates (for GRs). Winter/spring and summer demonstrated the highest/lowest differences between the seasons, results that suggest that implications for design might be aligned to the spatio-temporal distribution of precipitation patterns, and runoff regimes generated by snowmelt and rain-on-snow events, in locations where snowmelt represent high portion of runoff generation.

Transport infrastructure and manufacturing sector: an energy perspective from India

PurposeThe purpose of this study is to dissect the transport infrastructure performance, public spending in transport infrastructure development and the manufacturing sector in determining the transport sector energy consumption.Design/methodology/approachAn analysis of transport energy consumption with the transport infrastructure performance, public spending in transport infrastructure and manufacturing sector output in India using annual data for the period 1987–2019. The study used the autoregressive distributed lag (ARDL) bounds test approach along with FMOLS, DOLS and canonical cointegration regression (CCR) methods.FindingsThe results of the ARDL bounds test provide evidence for the long- and short-run relationships among study variables. It evidenced that transport infrastructure performance reduces transport energy consumption by using FMOLS, DOLS and CCR methods. Furthermore, the inference of the positive impact of value added in the manufacturing sector on transport energy consumption validates the higher energy demand of the manufacturing sector from a mobility perspective.Practical implicationsThe estimated finding of this study is expected to be contributing to policy-making discussions on transport infrastructure and manufacturing sector development in an emerging economy like India with insights on energy consumption.Originality/valueTo the best of the authors’ knowledge, this is the first study that integrates the impact of manufacturing sector output on transport sector energy consumption along with transport infrastructure performance and public investment in the transport infrastructure.

Parametric analysis of railway infrastructure for improved performance and lower life-cycle costs using machine learning techniques

Rigorous and efficient management of the railway infrastructure is crucial to avoid accidents and reduce operation and maintenance costs. This requires in-depth knowledge of the assets, the interaction among them and the effect that each track parameter has on the overall infrastructure performance. In this study, a large set of studies are carried out, on a previously calibrated finite element slab track model, where the relevant track parameters are varied within their usual ranges. The results are then used to train and validate a series of predictive models based on Machine Learning algorithms. This methodology provides greater understanding and enhanced prediction of the behaviour of tracks, which are composed of multiple variables such as the soil/subgrade, supporting layers, sleepers, pads and rails. The study also considers train axle loads and service speeds, which are other key elements that influence the track performance. The results show that the parameters that have greatest influence on the railway infrastructure are the properties of the soil, characteristics of the rail pads and the axle loads. This work can support the implementation of predictive maintenance procedures for railway tracks and the development of innovative technological solutions, providing responses to the industrial needs of reducing costs and contributing to improve the competitiveness of railway transport.

Causal inference on observational data: Opportunities and challenges in earthquake engineering

Collecting and analyzing observational data are essential to learning and implementing lessons in earthquake engineering. Historically, the methods that have been used to analyze and draw conclusions from empirical data have been limited to traditional statistics. The models developed using these techniques are able to capture associative relationships between important variables. However, the intervention decisions geared toward seismic risk mitigation should ideally be informed by an understanding of the causal mechanisms that drive infrastructure performance and community response. This article advocates for a paradigm shift in earthquake engineering where the language, tools, and models that have been (and continue to be) developed to draw causal conclusions from observational data are adopted. Several categories of data-driven earthquake engineering problems that can benefit from causal insights are examined. Two widely adopted frameworks from the broader causal inference literature are presented and linked to hypothetical earthquake engineering problems. The critical role of semi-parametric models and sensitivity analysis in justifying causal claims is also discussed. The article concludes with a discussion of specific opportunities and challenges toward the widespread use of causal inference as a tool for knowledge discovery in earthquake engineering. The ability to leverage the underlying physics of a problem within a causal inference framework is identified as both an opportunity and challenge for earthquake engineering researchers.

Water Infrastructure Performance in Sub-Saharan Africa: An Investigation of the Drivers and Impact on Economic Growth

There is a strong link between water infrastructure and development outcomes. As such, water infrastructure challenges could have an adverse effect on the economy at large. This study investigates the drivers of water infrastructure performance and analyzes how investment in water infrastructure affects economic growth, focusing on a panel of thirty-one Sub-Saharan African (SSA) countries. An integrative theoretical framework using panel regressions was developed. The result showed that an increase in water infrastructure performance due to a 1% increase in per-capita income growth and trade openness was 0.2% and 0.03%, respectively, and the constraint on water infrastructure performance due to a 1% increase in population density was 0.76%. The result showed that the impact is mostly driven by the effect of per-capita income growth and population density on lower- and middle-income countries. Our results also revealed that one additional increase in water infrastructure investment leads to a higher impact on economic growth. We further complement our study by investigating the policy interventions that the support water infrastructure outcome effect. We found that investment in water infrastructure along with the provision of credit to the private sector is a strong driver of economic growth; however, access to credit beyond a certain threshold—relative to the level of investment in water infrastructure in these countries—investment in water infrastructure would lead to an adverse negative macroeconomic effect. The policy implications of this study are discussed.

Gaussian Process Regression for Transportation System Estimation and Prediction Problems: The Deformation and a Hat Kernel

Gaussian process regression (GPR) is an emerging machine learning model with potential in a wide range of transportation system estimation and prediction problems, especially those where the uncertainty of estimation needs to be measured, for instance, traffic flow analysis, the transportation infrastructure performance estimation problems and transportation simulation-based optimization problems. The kernel function is the core component of GPR, and the radial basis function (RBF) kernel is the most commonly used one, suitable for tasks without special knowledge about the patterns of data, like trend and periodicity. However, an inappropriate hyperparameter of the kernel function may lead to over-fitting or under-fitting of GPR. During hyperparameter optimization, the usage of the RBF kernel often suffers from the issue of failing to find the optimal hyperparameter. This paper aims to address this problem by promoting the use of the hat kernel, which can reduce the risk of under-fitting. Moreover, we propose the notion of deformation, corresponding to severe over-fitting of a GPR. To further address this issue, we investigate the connection between deformation and the Bayesian generalization error of GPR. Two lower bounds for the hyperparameter of the hat kernel are also proposed to avoid deformation of GPR.

Implementasi RESTful untuk Mengurangi Mean Time to Resolve pada Alert Handling

One of the responsibilities of the company's IT infrastructure team is ensuring that IT services or services stay available. One part of analyzing the performance of IT infrastructure is MTTR (Mean Time to Resolve) or the average time necessary to restore a service that is experiencing issues. Warnings or alerts are shown when a service is encountering difficulties. As a member of the IT infrastructure team, the on-call engineer is responsible for managing alarms. At PT Sense Health Indonesia, difficulties manifest in the form of recurrence alerts, or warnings resulting from previously fixed or rectified issues. This warning is repeated with sufficient frequency to enhance the MTTR value. The findings demonstrated that the integration of tools using the RESTful approach streamlined the process of processing alerts. The deployment led to a 78% decrease in the average time required to handle alarms. This study contributes by demonstrating one method for decreasing MTTR for alert handling

Effect of Sustainable Infrastructure and Service Delivery on Sustainable Tourism: Application of Kruskal Wallis Test (Non-parametric)

This study examined the effect of sustainable infrastructure and service delivery on sustainable tourism in Kashmir Valley through the five main destinations by covering essential stakeholders. At present, tourism potential nations face destination sustainability issues in various junctures of the tourism sector. Therefore, international agencies and nations focus on sustainable tourism through sustainable transport infrastructure, other factors and improved service delivery. Few studies underlined the role of sustainable infrastructure in service quality and their collective impact on sustainability without empirical evidence at the destination level. At this juncture, the current study presents the summaries of empirical investigation of the effect of sustainable transport infrastructure and services delivery on sustainable tourism in Kashmir Valley. For analysis, the Kruskal-Wallis test (non-parametric) is applied to assess the variation in sustainable tourism based upon the performance of sustainable infrastructure and service delivery at destinations. The application of the Kruskal-Wallis independent samples tests duly confirmed the significant results. Sustainable tourism differs by transport infrastructure performance, other elements and service quality of the destinations. Therefore, it is to be stated that poor functioning of transport infrastructure and other basic structures adversely affects service delivery and raises sustainability issues at destinations. It asserts that the comprehensive sustainable transport infrastructure, water supply and drainage, health and sanitation, solid and liquid waste management are highly required to combat environmental issues and ensure sustainability at destinations. The findings of the study have significant policy implications to promote green infrastructure, quality services and development of sustainable tourism at destinations.