Performance Of Infrastructure Research Articles

Protecting infrastructure performance from disinformation attacks

Disinformation campaigns are prevalent, affecting vaccination coverage, creating uncertainty in election results, and causing supply chain disruptions, among others. Unfortunately, the problems of misinformation and disinformation are exacerbated due to the wide availability of online platforms and social networks. Naturally, these emerging disinformation networks could lead users to engage with critical infrastructure systems in harmful ways, leading to broader adverse impacts. One such example involves the spread of false pricing information, which causes drastic and sudden changes in user commodity consumption behavior, leading to shortages. Given this, it is critical to address the following related questions: (i) How can we monitor the evolution of disinformation dissemination and its projected impacts on commodity consumption? (ii) What effects do the mitigation efforts of human intermediaries have on the performance of the infrastructure network subject to disinformation campaigns? (iii) How can we manage infrastructure network operations and counter disinformation in concert to avoid shortages and satisfy user demands? To answer these questions, we develop a hybrid approach that integrates an epidemiological model of disinformation spread (based on a susceptible-infectious-recovered model, or SIR) with an efficient mixed-integer programming optimization model for infrastructure network performance. The goal of the optimization model is to determine the best protection and response actions against disinformation to minimize the general shortage of commodities at different nodes over time. The proposed model is illustrated with a case study involving a subset of the western US interconnection grid located in Los Angeles County in California.

Intelligent Novel IMF D-SWARA—Rough MARCOS Algorithm for Selection Construction Machinery for Sustainable Construction of Road Infrastructure

The quality of road infrastructure largely depends on the quality of road construction and adequate construction machinery. In order to reduce uncertainties and improve the performance of road infrastructure, it is necessary to apply modern and appropriate construction machinery. The aim of this study was to create a novel integrated multi-criteria decision-making (MCDM) model for the selection of pavers for the middle category of roads. A total of 16 criteria were defined and then divided into four main groups, on the basis of which the performance of 12 pavers was evaluated. Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA) with D numbers (IMF D-SWARA) was extended to determine the significance of the criteria for the selection of construction machinery based on two groups of experts. Rough measurement of choices and their ranking as a compromise solution (R-MARCOS) was used to evaluate and rank the performance of construction machinery. The results show that three alternatives out of the set considered can satisfy defined requirements. After that, we performed a multi-phase validity test in which different values of criterion weights were simulated. A comparative analysis with seven other Rough MCDM methods was also created, and the Spearman’s correlation coefficient (SCC) and WS coefficient were calculated to determine the correlation of ranks for sensitivity analysis and comparative analysis. Thus, the obtained results were verified.

The effect of transport infrastructure, congestion and reliability on mental wellbeing: a systematic review of empirical studies

ABSTRACT There has been a growing interest in understanding the interdependencies between urbanisation and mental health. Although transportation in cities is complex and of foremost importance to support the mobility of goods and passengers, little is known about how it relates to individual psychological distress. This review aims to provide an up-to-date synthesis of research evidence about the influence of transport infrastructure and operational performance (congestion, delays and reliability) on mental health/wellbeing. It is structured around three main interacting concepts that determined the search/selection of articles: identification of the above-mentioned transport-related exposures; use of psychological and physiological validated instruments; and the outcome on mental health/wellbeing. Ultimately, 69 studies were identified involving an empirical quantitative focus that met the inclusion criteria. We summarise the instruments most reported in these studies and the findings linking transport indicators and psychological and physiological outcomes. Across the review, we identified evidence of the contribution of key transport infrastructure, congestion and delay indicators on negative affective states and psychophysiological distress. Regarding transport reliability, the scarce number of studies identified did not allow for drawing similar firm conclusions. We conclude by discussing some limitations and providing recommendations for future research and policy-making agendas.

Application of Unmanned Aerial Technologies for Inspecting Pavement and Bridge Infrastructure Assets Conditions

Infrastructure in the U.S.A. was graded with a “C–” in the 2021 ASCE report card. Frequent monitoring of infrastructure assets is key to ensuring the serviceability of the assets and safety of public users. Advancements in aerial technologies, compact sensors, computer processing, and image analysis software have given rise to various engineering applications of sensor-mounted unmanned aerial vehicle (UAV) platforms. Several contactless sensors are being mounted on UAVs to conduct a comprehensive assessment of infrastructure conditions compared to a qualitative visual examination by an experienced inspector. Many transportation agencies in the U.S.A. have been using UAVs for applications ranging from the research stage to regular inspection activities. These applications are mainly focused on inspecting infrastructure assets such as pavements, bridges, substructures, railways, and other assets. The current research discussed the approaches followed to conduct aerial condition monitoring of transportation infrastructure assets using optical sensors mounted on UAVs. Qualitative and quantitative inspections of pavement, bridge, and substructure infrastructure case studies were performed. Pavement distress extents were measured in three dimensions and a 360° inspection of bridges was conducted by accessing hard-to-reach areas. Scaled views of the four sides of a bridge were developed and the challenges in data collection and processing were outlined. Further, a localized inspection was also demonstrated to show the feasibility of using photogrammetry for remote condition assessments post-disaster/emergency. Overall, the rich visualization, safety, flexibility, and ease of handling offered by these technologies are expected to transform the method of conducting infrastructure performance monitoring inspections in the future.

Data strategies for improving infrastructure value and performance in New Zealand

This paper reviews three digitalisation projects in the Building Innovation Partnership, an industry-led research and innovation initiative based at the University of Canterbury, New Zealand, focused on improving the transfer of infrastructure information within and between organisations. The building information modelling (BIM) to building asset management project focuses on the use of client-facing information managers and asset information specification tools to improve information transfer between building designers, contractors and facility/asset managers. The Infrastructure Asset Data project focuses on the use of metadata standards to federate and analyse data on three waters pipe networks managed by different councils. The BIM-Based Building Consenting project focuses on using BIM data to automate the assessment of building designs against the requirements of digitised building regulations and standards. Case studies associated with the three projects show that the capability of industry to transfer digital information needs to be lifted, to exploit the benefits of digital information, processes and technologies in the construction and infrastructure sectors.

Optimising sustainable mobility: A performance assessment of non-motorised transport infrastructure in Johannesburg, South Africa

Transportation systems are somewhat incomplete without integrating non-motorised transport (NMT) facilities to ensure safety, security, convenience, productivity and reduced environmental impact from such projects. This study examined the performance of NMT infrastructure and services provided to ensure smart transport and mobility in Johannesburg, South Africa. Two projects were identified as case studies, and a total of 21 semi-structured, open-ended interviews were conducted with residents and other road users in the vicinity of the projects to enable the acquisition of relevant information. Data was analysed using thematic content analysis supported with Atlas-ti software. Findings revealed that, although the facilities are in good condition and adequately maintained, the usage level is low. Incidents of poor safety and security were reported. Nevertheless, new value-added businesses operate along the routes and users indicated some satisfaction with their travel experience. To achieve value creation where NMTs are provided, it is crucial to ensure that the "smartness" of the cities is achieved and sustained. The practical implication of this study is the provision of invaluable information towards assisting road and NMT infrastructure stakeholders in improving the planning and delivery of sustainable transport infrastructure. Future studies will benefit from using more case study projects to strengthen these findings further and improve the generalisability.

Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this article, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60 percent reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenarios.

Perancangan Sistem Pakar Untuk Menilai Kinerja Irigasi

Irrigation is a supporting infrastructure for water distribution in agriculture. Its role is vital, especially for agricultural areas which are not rain-fed, meaning that to irrigate agricultural areas the irrigation infrastructure is needed. It is important for all parties involved in agriculture and irrigation management institutions to find out and ensure the condition and performance of an irrigation network, whether its function is still as planned or even vice versa. So far, the performance evaluation of irrigation networks is still carried out conventionally and only reaches a form of Maintenance Operation report, not specific to its performance. So based on these conditions, this research will build an expert system that can be used to assess the performance of irrigation networks. The design begins by determining the logic of assessment that will be used as a way of thinking from the expert system that will be built. The indicators used by the system to assess irrigation performance are as stated in the regulation of the Ministry of Public Works and Public Housing No. 12 of 2015. After designing the expert system, the next step is to simulate it using the data available in the operation and maintenance reports of some irrigation networks that are used as samples. Then the simulation results are analyzed so that it can be known whether the system can work as planned or not. The system simulation results show a mismatch between the two indicators namely the performance of infrastructure and water services. While other assessment indicators have logical results.

MULTICRITERIA TRAIN RUNNING MODEL AND SIMULATOR FOR RAILWAY CAPACITY ASSESSMENT

Railway line capacity is growing in importance as a criterion for the assessment of railroad infrastructure performance. This problem is becoming more and more relevant as the demand for rail transport increases, especially considering the transport policy related to the promotion of climate-neutral means of transport. Insufficient capacity affects the stability and reliability of railway traffic operations. The analytical methods used for capacity estimation are typically insufficient to solve problems of a multicriteria nature (i.e. problems which take traffic heterogeneity and human factors into account). Optimisation methods, on the other hand, usually yield the best results if the current timetable for a given line is known. Additionally, they do not strongly consider the impact of the running characteristics of a specific train type and of the system or several systems in operation on the line (e.g. national Class B system and ERTMS/ETCS system). Therefore, this paper proposes a model and a simulation program developed in the Matlab and Simulink environment to be used to simulate on-route train movement, to study railroad capacity with different control systems, as well as for predictive train control to minimise energy losses. The authors described the assumptions adopted for the simulation program and the input parameters configurable against a specific line segment. They also discussed selected results derived from simulations of controlling the departure of trains to a railway line with the purpose of energy loss reduction.

Kentleşme Olgusu, Kanal İstanbul ve Ulaştırma Kaynaklı Ekonomiye Etkisinin Analizi

Urban planning has a vital role in terms of social, economical, trading, political, transport and other services, cultural and infrastructural performance of urban. There are serious effects of mega projects to urban as input and output. Hence quality of projects are as significant as quantity of projects too. In this context in this paper firstly urban development in Istanbul is evaluated on the parallel of specific samples. Then economical potential of Istanbul and Turkey is analyzed on the context of Channel Istanbul. After these economical, trading, social and political intuitives and influence areas of Turkey on regional and global scale are detailed examined in terms of transportation. Lastly conclusions and recommendations are. presented.

Deep reinforcement learning-based decision support system for transportation infrastructure management under hurricane events

The high wind, large surge/wave and heavy rainfall during a hurricane may significantly impact the performance of transportation infrastructure and associated traffic safety. Accordingly, stakeholders need to make a sequence of decisions to close (or restrict) the traffic of vulnerable components in the transportation network (e.g., aerodynamics-sensitive long-span bridges, hydrodynamics-sensitive coastal bridges and inundation-sensitive road segments) for the balance of safety and mobility. Due to large uncertainty involved in hurricane weather and traffic conditions, it is essentially a stochastic sequential decision problem and can be effectively formulated as a Markov decision process. This study proposes a deep reinforcement learning (RL)-based decision support system for stakeholders to optimally manage these critical components for the purpose of minimizing the network-level losses induced by hurricanes. Specifically, the RL policy (i.e., mapping from high-dimensional continuous traffic/weather information to management decisions) is represented by a deep neural network (DNN) and the optimal decision (corresponding to a set of DNN weights) is obtained using the deep Q learning algorithm. A case study based on a hypothetical traffic network is utilized to demonstrate the good performance of the developed deep RL-based decision support system in the application of transportation infrastructure management under hurricane conditions.

Decision-Making Framework for Prioritizing the Improvement of Pedestrian Facilities in Urban Areas Using Integrated Delphi, AHP, and TOPSIS Approach

The existing pedestrian facilities in urban areas can be improved by prioritizing the areas based on their infrastructure performance, location, safety, mobility, and liveability aspects. This paper aims to present a combination of Delphi, analytic hierarchy process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing the improvement of pedestrian facilities in an urban area. The approach has been broken into three stages. Firstly, the significant factor assessment was done by the Delphi method. Secondly, AHP was applied to estimate the relative weightage of each factor. Finally, the TOPSIS method was used to rank the alternatives in preparation for the final decision. For better understanding, the proposed method was demonstrated by applying the framework in a case study. The analyses revealed that the proposed method is effective for prioritizing the improvement of pedestrian facilities in an urban area. The method described in this paper is intended to assist decision makers in ranking different sidewalk stretches in the urban areas that need to be improved based on various attributes. This paper makes a major impact by incorporating a novel approach for the decision making of pedestrian facilities improvement, which considered both subjective and objective judgments of multiple decision makers in the procedure at various stages using different multi-criteria decision making techniques. This approach can be combined with a scheduling methodology and optimization technique to increase the efficiency of the resource allocation process.

Cross-asset prioritization model for transportation projects using multi-attribute utility theory: a case study

The Moving Ahead for Progress in the 21st Century Act (MAP-21), enacted by the US Congress in 2012, establishes seven national infrastructure performance goals that must be considered during funding allocation procedures by state transportation agencies (STAs). These goals’ wide range of aspects creates situations where STAs have to deal with conflicting objectives without formal trade-off mechanisms, forcing them to make investments that might be difficult to justify to stakeholders. Moreover, STAs strive to create standard performance metrics that allow the comparison of investment alternatives among different groups of assets. This paper presents a case study on applying a Multi-Attribute Utility Theory (MAUT) model to make trade-offs among conflicting objectives in different types of transportation construction projects. The case study was conducted on ten construction projects executed by the Iowa Department of Transportation. The case study shows how the proposed MAUT model can be used as an objective mechanism to prioritize infrastructure projects across asset classes to provide the necessary justification for infrastructure policy decision-making.

Investigating temporal dynamics of urban densification on the buried water infrastructure performance

Urban densification has dominated the land uses and land covers which may jeopardize the quality of life and sustainable development. Moreover, urban densification process exerts stress on buried water infrastructure (BWI) performance. In this study, the need of predicting the change in urban densification and its subsequent impacts on BWI performance over time has drawn attention to conduct a temporal analysis. A hybrid approach was used to select the significant control performance indicators (CPIs) and conduct a temporal analysis. Eight scenarios were generated using the Canadian Council of the Ministers of the Environment Water Quality Index approach to address the change in drinking water quality. The proposed approach was implemented in a municipality of British Columbia, Canada, as a case study. The results showed a 40 % decrease in the BWI performance due to the urban areas have rapidly grown both in population and built-up areas over three decades. Furthermore, an ideal situation was assumed to show that the targeted service level (i.e., 0.5) can be achieved with 3 % to 5 % improvement in every five years of service level. These findings will assist the decision makers to understand the linkage between CPIs to maintain and achieve the required BWI performance.

An Integrated DPSIR-SD Framework for Sustainability Assessment of Roads in Australia

The Australian Sustainability Development Goals (SDGs) Summit in 2018 attracted much-needed national attention towards environmental goals and targets compared with other aspects of sustainability. Road infrastructure is the backbone of modern society and plays a crucial role in accomplishing a targeted balance between these aspects of sustainability and achieving the SDGs. This article presents an integrated sustainability performance assessment methodology that acts as a decision support tool. A series of two conceptual modelling techniques—drivers—pressure—state—impact—response (DPSIR) and system dynamics (SD)—is employed, with the cause-and-effect relationships of the sustainability indicators developed utilising the DPSIR framework, and a quantitative analysis carried out through a subsequent SD model. The end result is the generation of a Sustainability Performance Index (SPI) for road infrastructure created by analysing the SD model and DPSIR index layer relationship. The benefits and applicability of the proposed methodology are validated through case study analysis. The overall aim is to determine restricting factors and response strategies influencing road infrastructure and transport sustainability performance during the operation and maintenance phase. Thus, a significant contribution is made through the proposed methodology for assessing factors influencing the long-term achievement of the SDGs.

Fault aware intelligent resource allocation using Big Bang-Big Crunch trained neural network for cloud infrastructure

This Ongoing COVID-19 epidemic situation, which has resulted in the loss of lives and economics. In this scenario, social distancing is the only way to prevent ourselves. In such a scenario to boost the economy, a globally large number of industries and businesses have shifted their system to cloud-like education, shipping, training and many more globally. To support this transition cloud services are the only solution to provide reliable and secure services to the user to sustain their business. Due to this, the load over the existing cloud infrastructure has drastically increased. So it is the responsibility of the cloud to manage the load over the existing infrastructure to maintain reliability and serve high-quality services to the user. Task allocation in the cloud is one of the key features to optimize the performance of cloud infrastructure. In this work, we have proposed a prediction-based technique using a pre-trained neural network to find a reliable resource for a task based on previous training and history of cloud and its performance to optimize the performance under the overloaded and under loaded situation. The main aim of this work is to reduce the fault and provide high performance by reducing scheduling time, execution time and network load. The proposed model uses the Big Bang Big Crunch algorithm to generated huge datasets for training our neural model. The accuracy of the BB-BC-ANN model is improved with 98% accuracy.

Development and Application of an Integrated Monitoring and Early Warning Platform for Land Transportation Infrastructure under Special Engineering Conditions

To ensure the safety service performance of land transportation infrastructure under special engineering conditions, centering on the characteristics of structures such as the road high-steep slope, high embankment, special subgrade, railway track, and airport pavement, an integrated monitoring and early warning platform based on health monitoring and safety evaluation was built through the combined design of the safety indicator module, monitoring data module, information processing module, numerical simulation module, inversion prediction analysis module, and safety early warning module. The data interaction between the integrated platform and five monitoring subplatforms was realized by the B/S mode, and the related function interfaces were established. The research results show that as the monitoring data storage analysis and early warning carrier, the integrated monitoring and early warning platform not only presents key data such as comprehensive early warning information, evaluation indicators, and monitoring elements but also realizes the intelligent, automatic, and visual presentation of monitoring data. In addition, relying on the high-steep slope of the Hohhot-Bikeqi Highway (section: K528+215~K528+525), through the application of the high-steep slope monitoring and early warning subplatform system in the practical project, it is verified that the platform has a good monitoring and early warning effect, which provides a reliable platform guarantee for the health monitoring and safety operation of the project.

As Data Flows Surge Data Infrastructure Groans

As Data Flows Surge Data Infrastructure Groans

Model-scale tests to examine water pressures acting on potentially buoyant underground structures in clay strata

Throughout the service life, underground structures are subjected to transient and sustained hydrostatic pressures. The reservoir impoundment results in an increase in water level, as well as hydraulic gradient, which can endanger the uplift performance of infrastructure. In uplift design, a reduction factor is often suggested for buoyant force acting on underground structures in clays due to the time lag effect. However, the mechanism of pore pressure generation in clays is not fully understood. This investigation presents a novel U-shaped test chamber to assess the pore pressure generation with time in the horizontal branch subjected to an increase in reservoir level in the left vertical branch. A mathematical model is developed to explain the time lag effect of pore pressure generation. The test program also involves the evaluation of uplift pressure acting on foundation model in the right vertical branch due to adjacent reservoir impoundment. It is found that the time lag effect of pore pressure generation in clays can be observed irrespective of hydraulic gradient, but a higher hydraulic gradient can lead to a faster response in pore pressure sensors. A reduction factor of 0.84–0.87 should be considered to reduce the conservatism of uplift design.

A seismic emergency performance optimization model for infrastructure systems under demand differences: A case study in China

AbstractInfrastructure systems play a crucial role in ensuring the safety of cities and the well‐being of residents after earthquakes. Meanwhile, infrastructure systems are vulnerable to earthquakes and may fail to provide necessary services, highlighting the significant need to improve seismic emergency performance. Nevertheless, the seismic emergency performance of infrastructure systems still lacks effective enhancement strategies and optimization models, which makes it challenging to devise and benchmark appropriate emergency enhancement actions. This study proposes an emergency performance optimization model for infrastructure systems against earthquakes. The model aims at maximizing the effects of resistance actions and short‐term recovery actions on infrastructure systems with consideration of residents' expectations of infrastructure performance after earthquakes. The efficacy of the proposed model is tested by a case study in China. Experiments and results illustrate advantages of the seismic emergency performance optimization and prioritize resistance and short‐term recovery activities within constraints set by available resources.