Performance Of Infrastructure Research Articles

Projected performance of green infrastructure strategies for flood mitigation in the Ganges-Brahmaputra-Meghna delta

Background: The Ganges-Brahmaputra-Meghna (GBM) delta – the world’s most populous river delta – faces heightened susceptibility to the rise in flooding disasters due to climate change, impacting millions annually. Current flood management strategies are unsustainable and ineffective, and resilient flood management is needed. A promising alternative is the strategic implementation of green infrastructure (GI) applications, which have proven effective in flood management in other regions. Methods: An analysis of the region’s past and future vulnerability to flooding is conducted. Then, green infrastructure performance metrics from regions with similar climatic conditions are extrapolated for the GBM. Green roofs, permeable pavements, and rain gardens were identified as the most suitable GI types for the GBM. Finally, computer simulations were employed to analyze the performance of different implementations of GI within a model city. Results: The simulations showed that 0% green rooftop coverage, 100% permeable pavement coverage, and 40% rain garden coverage were the most feasible GI layout. This configuration resulted in the most preferable balance between cost effectiveness and reduced runoff. Green rooftops were minimized due to high installation costs relative to their retention capacity, whereas permeable pavements and rain garden coverage were maximized. Conclusions: The studies show GI’s potential for flood mitigation and resilience in the GBM region. GI initiatives align with the region's flood mitigation policies and are thus feasible to implement with aid from government incentives. Furthermore, the computer program developed for this analysis could serve as a valuable tool for assessing GI implementation limits and offering guidance to policymakers.

Conditional adaptive time series compensation and control design for multi-axial real-time hybrid simulation

The structural performance of critical infrastructure during extreme events requires testing to understand the complex dynamics. Shake table testing of buildings to evaluate structural integrity is expensive and requires special facilities that can allow for the construction of large-scale test specimens. An attractive alternative is a cyber-physical testing technique known as Real-Time Hybrid Simulation (RTHS), where a large-scale structure is decomposed into physical and numerical substructures. A transfer system creates the interface between physical and numerical substructures. The challenge occurs when using multiple actuators connected with a coupler (i.e., transfer system) to create translation and rotation at the interface. Tracking control strategies aim to reduce time delay errors to create the desired displacements that account for the complex dynamics. This paper proposes two adaptive control methodologies for multi-axial real-time hybrid simulations that improve capabilities for a higher degree of coupling, boundary, complexity, and noise reduction. One control method integrates the feedback proportional derivative integrator (PID) control with a conditional adaptive time series (CATS) compensation and inverse decoupler. The second proposed control method is based on a coupled Model Predictive Control (MPC) with the CATS compensation. The performance of the proposed methods is evaluated using the virtual multi-axial benchmark control problem consisting of a steel frame as the experimental substructure. The transfer system consists of a coupler that connects two hydraulic actuators generating the translation and rotation acting at the joint. Through sensitivity analysis, parameters were tuned for the decoupler components, CATS compensation, and the control design for PID, LQG, and MPC. Comparative results among different control methods are evaluated based on performance criteria, including critical factors such as reduction in the time delay of bothactuators. The research findings in this paper improve the tracking control systems for the multi-axial RTHS of building structures subjected to earthquake loading. It provides insight into the robustness of the proposed tracking control methods in addressing uncertainty and improves the understanding of multiple output controllers that could be used in future cyber-physical testing of civil infrastructure subjected to natural hazards.

Conditional seasonal markov-switching autoregressive model to simulate extreme events: Application to river flow

Extreme events have the potential to significantly impact transportation infrastructure performance. For example, in the case of bridges, climate change impacts the river discharge, hence scouring patterns, which in turn, affects the bridge foundation stability. Therefore, extreme events (river flow) forecasting is mandatory in bridge reliability analysis. This paper approaches this river flow forecasting problem by developing a Markov-Switching Autoregressive model coupled with a conditional hidden seasonal Markov component. In addition, the proposed model is also combined with the deep machine learning neural networks method to forecast river flow from a dataset or from simulations. The proposed method is illustrated by using realistic data: historic river flow values of the Thames River. The results indicate that the proposed model well represented the extreme events within the dataset. In terms of river flow forecasting, the results indicate that the forecasts improve when the training period changes from 20 years to 40 years.

Geotechnical assessments for renewable energy infrastructure: Ensuring stability in wind and solar projects

Geotechnical assessments are crucial for ensuring the stability and longevity of renewable energy infrastructure, particularly in wind and solar projects. This review explores the significance of geotechnical assessments in these projects, highlighting key considerations and challenges. Geotechnical assessments play a critical role in the design, construction, and operation of renewable energy infrastructure, providing essential information about the subsurface conditions that can impact the stability and performance of wind and solar projects. These assessments involve the evaluation of soil, rock, and groundwater conditions to assess their suitability for supporting renewable energy structures. In wind energy projects, geotechnical assessments are essential for determining the foundation design of wind turbines. The soil and rock conditions at the site can significantly impact the stability and load-bearing capacity of the foundation, affecting the overall performance and safety of the turbine. Similarly, in solar energy projects, geotechnical assessments are necessary for designing the foundation of solar panels and support structures, ensuring they can withstand environmental loads and maintain their efficiency over time. One of the key challenges in geotechnical assessments for renewable energy projects is the variability of subsurface conditions. Soil and rock properties can vary significantly over short distances, requiring detailed site investigations to accurately characterize the subsurface conditions. Additionally, the presence of natural hazards such as landslides, earthquakes, and floods can further complicate geotechnical assessments, necessitating robust risk mitigation strategies. Despite these challenges, geotechnical assessments are essential for ensuring the long-term stability and performance of renewable energy infrastructure. By providing valuable insights into subsurface conditions, these assessments help developers and engineers make informed decisions about site selection, foundation design, and risk management, ultimately contributing to the successful implementation of renewable energy projects. In conclusion, geotechnical assessments are vital for ensuring the stability and longevity of renewable energy infrastructure, particularly in wind and solar projects. By providing crucial information about subsurface conditions, these assessments help mitigate risks and ensure the safe and efficient operation of renewable energy projects. Keywords: Geotechnical Assessments, Renewable Energy, Infrastructure, Stability, Wind and Solar Projects.

Evaluation of the Impact of Climate Variability on the Soil-Water Characteristics Curve

The hydro-mechanical behavior of unsaturated soil, particularly expansive soil, is influenced significantly by cyclic wetting and drying. Understanding the soil parameters is crucial when evaluating the performance of infrastructures constructed on expansive clay. As a result of extreme rainfall events, highway slopes containing highly expansive Yazoo clay in Mississippi, U.S., become vulnerable to volume change. The phenomenon creates perched water zones within the slopes and poses a risk of slope failure. The soil-water characteristic curve (SWCC) defines the relationship between water content and soil suction, which can be obtained from different laboratory procedures. However, conventional laboratory methods have some limitations. To address this, various analytical and predictive models have been developed, but they can only offer estimates based on soil characteristics and lack seasonal variations occurring in field conditions. Studying seasonal SWCC through field measurements can help understand soil responses to changing moisture conditions. The current study utilized field data from six highway slopes in Mississippi and classified the data into different seasons: spring, summer, and fall. After obtaining van Genuchten parameters from the fitted curve for each season, the finite element method was applied to evaluate the parameters for accurate numerical analysis of infrastructures containing expansive clay. The study observed the variations in flow parameters with seasonal change that cannot be achieved when data from only one season is considered. The findings underscore the importance of field instrumentation data for developing SWCC and the significance of seasonal flow parameters in infrastructure design.

Technology Infrastructure and Business Performance of Commercial Banks in Kenya

The general objective of the study was to examine the role of technology infrastructureon business performance of Commercial Banks in Kenya. The philosophy that guided the research is positivism philosophy. The study adopted correlational research design. The target population was commercial Banks in Kenya register by the Central Bank of Kenya. The population consisted of all 42 commercial banks in Kenya. Respondents’ population comprised of five top managers from each bank translating to 210 top managers. Slovin’s formula was used to calculate the sample size. Purposive sampling technique was used to select 138 top managers of the 42 commercial Banks in Kenya. This study used a self-administered, closed and open-ended questionnaire to obtain primary data. A pilot study was conducted to test the validity and reliability of the data collection instrument. Quantitative data was collected and analyzed in this study by calculating the response rate with descriptive statistics such as mean, standard deviation, median and proportions using the Statistical Package for Social Sciences (SPSS) version 24). Regression analysis and correlation analysis was used to carry out inferential data analysis to determine the direction and strength of the relationship between the independent and the dependent variables. In order to test the influence of information technology capability on business performance of Commercial Banks in Kenya, the study employed a hierarchical regression analysis with moderation. The study results were presented through use of tables and figures. The study concludes that technology infrastructure has a positive and significant effect on business performance of Commercial Banks in Kenya. The study revealed that hardware/Software, network and database influence business performance of Commercial Banks in Kenya. This implies that improvement in information technology infrastructure (hardware/Software, network and database) would lead to improvement in business performance of Commercial Banks in Kenya. The study recommends that the management of commercial banks in Kenya should ensure they had adequate and up to date hardware/Software to enable their employees improve their productivity.

Preface

GeoShanghai 2024 International Conference Volume 3: Transportation Geotechnics Shanghai, 26-29 May, 2024 GeoShanghai 2024 International ConferenceCollege of Civil Engineering, Tongji University, 2024All Rights ReservedGeoShanghai is a series of international conferences on geotechnical engineering held in Shanghai quadrennially. The conference was inaugurated in 2006 and was successfully held in 2010, 2014 and 2018, with more than 1600 participants in total. Since the last conference, the geotechnical community has witnessed many advances both in fundamental understanding and engineering practices. To demonstrate the latest developments and promote collaborations in geotechnical engineering and related fields, we launch the 5th GeoShanghai International Conference to be held in May 2024.Transportation geotechnics is a vitally interdisciplinary aspect encompassing engineering, geology, and materials science, exploring the durability, safety, and efficiency of transportation networks. Within this domain, pavement materials and structures, railroad engineering, airfield construction, and pipeline infrastructure represent the focal points. Pavements, serving as crucial interfaces between vehicles and the ground, demand attention to material properties and structural integrity. Railroads require resilient tracks capable of withstanding dynamic loads, while airfields necessitate robust pavements to support heavy aircraft. Similarly, pipelines rely on reliable foundation designs to ensure stability and mitigate risks. This topic delves into the latest advancements, challenges, and innovations within transportation geotechnics, with a specific focus on these critical themes. Through collaborative discussions and knowledge exchange, we aim to enhance the resilience, sustainability, and performance of transportation infrastructures worldwide.This volume received a total of 47 papers from countries such as China, United States, Russia, Australia, Thailand, Portugal and others, totaling 41 papers accepted. All papers were reviewed and the accepted papers will be submitted to IOP Conference Series: Earth and Environmental Science indexed by Scopus.Cordially yours,Feipeng Xiao, David Connally, Peerapong Jitsangiam, Lin Cong, Fan Gu, Yao ShanThe editors of Volume 3: Transportation GeotechnicsList of Tracks, Conference Steering Committee, International Advisory Committee, Technical Committee, Organizing Committee are available in this pdf.

Sensitivity analysis of the calibration factor resulting from the application of the HSM predictive method to Italian rural two-lane, two-way roads. The Emilia-Romagna case study

The present study investigates the adoption of predictive methods for road safety in the Italian context. The main goal of predictive methods is to describe safety performances of a road infrastructure according to the real number of occurred crashes and the actual infrastructure characteristics. Currently, the most reliable and widely used model is reported in the Highway Safety Manual (HSM), initially developed for the United States. In order to adapt the HSM existing model to the Italian context a calibration procedure is required. The main purpose of this study is to conduct the sensitivity analysis of the calibration factor, to evaluate the influence of various road characteristics (expressed in terms of Crash Modification Factors – CMFs) on the reliability of the model. Results show that the most influential factors are represented by planimetric curve radius, followed by shoulder type and width and Roadside Hazard Rating (RHR). In addition, the Italian roads taken into account for the present case study, which are single carriageway rural roads located in Emilia Romagna region, have generally exhibited more critical conditions compared to American roads. As a consequence, CMFs expressing the aforementioned infrastructure characteristics are typically greater than 1. Furthermore, road features and driving habits vary significantly with the orographic context (mountain, hill, or valley) and geographical area, factors that cannot be neglected when calibrating predictive models. As a result, a layered calibration would bring to the most reliable solution and, in specific situations, alternative predictive models to HSM could be developed.

The Effects Of Overtopping On Green/Grey Infrastructure

This paper presents results of a reduced (1:8) scale experiment investigating the performance of hybrid structural (gray) and natural-based (green) infrastructure for wave overtopping reduction. Experiments were scaled to a 1:8 geometric scale based on 1:2-scale experiments conducted during the Summer of 2023 at Oregon State University. Seven wave conditions were tested, with (model-scale) wave periods ranging from 1 to 2 seconds and wave heights ranging from 6.0 to 7.5 cm. These wave conditions were conducted throughout two configurations: a seawall-only (baseline) configuration and a configuration with the seawall in combination with a mangrove forest installed seaward of the wall. The total volume of overtopped water was measured for each wave condition. Results indicated that adding mangroves reduced overtopping for all wave conditions, with an average of 32.1% reduction in overtopped volume compared to the baseline configurations. This reduction falls within the range of preexisting overtopping rates. Results from these experiments can assist engineers in understanding the performance of hybrid coastal infrastructure to design effective and sustainable shoreline protection.

Identifying factors that influence electric vehicle charging station performance in expanding networks.

Charging infrastructure deployment has taken off in many cities with the rise of the number of electric vehicles on the road. Expansion of infrastructure is a matter of prioritisation of resources to optimise the infrastructure. This paper explores how to measure charging station performance, to address the challenges that policy makers face. These performance indicators are used in a regression model, based upon current utilisation of the network, to predict which charging stations perform best. The results show that a model based on available geographical data and performance metrics of the current network are best combined to predict infrastructure performance. The variability between public charging stations is however big, as frequent user characteristics do determine the performance to a large extent.

A deep dive into green infrastructure failures using fault tree analysis

Green Infrastructure has transformed traditional urban stormwater management systems by fostering a wide range of service functions. Despite their popularity, green infrastructure's performance can deteriorate over their lifecycle, leading to operational failures. The operation of green infrastructure has predominantly relied on reactive maintenance strategies. To anticipate malfunctions and enhance the performance of green infrastructure in the long run, failure data needs to be recorded so that deterioration processes and component vulnerabilities can be recognized, modelled and included in predictive maintenance schemes. This study investigates possible failures in representative GIs and provides insights into the most important events that should be prioritized in the data collection process. A method for qualitative Fault Tree Analysis using minimal cut sets are introduced, aiming to identify potential failures with the minimum number of events. To identify events of interest fault trees were constructed for bioswales, rain gardens and green roofs, for three groups of service function failures, namely runoff quantity control, runoff quality control and additional service functions. The resulting fault trees consisted of 45 intermediate and 54 basic events. The minimal cut set analysis identified recurring basic events that could affect operation among all three green infrastructure instances. These events are ‘trash accumulation’, ‘clogging due to sediment accumulation’, and ‘overly dense vegetation’. Among all the possible cut sets, events such as ‘plants not thriving’, ‘invasive plants taking over’, and ‘deterioration caused by external influences’ could potentially disrupt most of the service functions green infrastructure provides. Furthermore, the analysis of interactions between component failures shows vegetation and filter media layer failures have the highest influence over other components. The constructed fault trees and identified basic events could be potentially employed for additional research on data collection processes and calculating the failure rates of green infrastructure and as a result, contribute to a shift toward their proactive operation and maintenance.

Enhancing Road Infrastructure Performance in Kisii County, Kenya: The Critical Role of Monitoring and Evaluation Tools

Enhancing Road Infrastructure Performance in Kisii County, Kenya: The Critical Role of Monitoring and Evaluation Tools

Eco-efficient asphalt recycling for urban slow mobility

Cycling infrastructures contribute to advancing zero-impact transport systems, aligning with the European Commission’s proactive climate change mitigation policies. This paper explores the potential of innovative and sustainable pavements for cycling paths with mixtures composed of road-milling materials. This investigation involves low-environmental-impact bituminous-based mixtures differing from recipe, mixing method, and laying. Up to 100% secondary aggregates are used as alternative materials to design the Grande Raccordo Anulare delle Biciclette (GRAB), a 44-km cycling ring in Rome. According to the European standard EN 15804, their “from cradle to gate” life cycle analysis allows a comprehensive assessment and comparison of the environmental impact. Core and additional environmental impact categories and resource use indicators were quantified using primary data from asphalt producers and secondary data from the Ecoinvent database in the SimaPro software. Within the H2020 InfraROB project (grant agreement no. 955337), which aims at enhancing road infrastructure integrity, performance, and safety through autonomous robotic solutions and modularization, experimental sections have been constructed using a cold-mixed asphalt composed entirely of recycled asphalt and a rejuvenating additive. The results underscore the potential of the examined low-impact approach in conserving Earth’s resources, ensuring long-lasting infrastructure for vulnerable urban populations and fostering sustainable environmental management.

A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming

The sharing of mobile network infrastructure has become a key topic with the introduction of 5G due to the high costs of deploying such infrastructures, with neutral host models coupled with features such as network function virtualization (NFV) and network slicing emerging as viable solutions for the challenges in this area. With this in mind, this work presents the design, implementation, and test of a flexible infrastructure-sharing 5G network architecture capable of providing services to any type of client, whether an operator or not. The proposed architecture leverages 5G’s network slicing for traffic isolation and compliance with the policies of different clients, with roaming employed for the authentication of users of operator clients. The proposed architecture was implemented and tested in a simulation environment using the UERANSIM and Open5GS open-source tools. Qualitative tests successfully validated the authentication and the traffic isolation features provided by the slices for the two types of clients. Results also demonstrate that the proposed architecture has a positive impact on the performance of the neutral host network infrastructure, achieving 61.8%-higher throughput and 96.8%-lower packet loss ratio (PLR) in a scenario sharing the infrastructure among four clients and eight users when compared to a single client with all the network resources.

Evaluation of Locally Available Calcined Clay-Based Geopolymer for the Stabilization of Expansive Soils

Geopolymers (GPs) have been effectively used as an alternative to traditional calcium-based stabilizers for treating problematic expansive soils over the last decade. However, the high cost of precursors and limited availability of traditionally manufactured GPs pose significant challenges for commercial implementation. Utilizing locally available sources to manufacture GPs could address such limitations and promote sustainable development. A research study was designed and implemented to evaluate the efficacy of locally available calcined clay-based GP stabilizers to improve the performance of expansive soils and the results were compared with lime-treated and untreated soils. An array of laboratory studies was conducted using two natural clayey soils and two types of GPs to assess improvements in several engineering properties, including unconfined compressive strength before and after capillary soaking, resilient moduli, and moisture-induced strains. Supplemental microstructural studies using X-ray diffraction, optical microscopy, and scanning electron microscope imaging were conducted to identify the mineralogical changes and detect the precipitation of new reaction products. The engineering studies demonstrated that the application of GP significantly enhanced the strength, durability, and stiffness while reducing the swelling and shrinkage strains in the GP-treated soils. Microstructural analysis revealed that the precipitated GP gels effectively coated the soil particles and provided a uniformly bonded GP–soil matrix that enhanced the engineering performance. Overall, this study provides a comprehensive understanding with respect to the potential of using locally available calcined clay-based GPs as a sustainable and durable alternative in enhancing the engineering properties of expansive soils for the long-term performance of transportation infrastructures.

Efficiency Evaluation and Performance Governance of Infrastructure Construction Based on DEA and Improved Neural Networks

Abstract Infrastructure construction is the carrier of regional economic development, and the rational use of urban construction land is an important basis that largely depends on the efficiency of urban infrastructure construction. However, due to the lack of effective integration between urban evaluation and management in infrastructure, the infrastructure performance evaluation system is not yet mature enough. Therefore, this article conducts efficiency evaluation and performance governance of infrastructure construction based on DEA and improved neural networks. Firstly, based on a comprehensive analysis of performance evaluation, the advantages and disadvantages of infrastructure construction were explored. Secondly, in the study of using DEA and improved neural networks to evaluate the efficiency of infrastructure construction, a performance evaluation index system for urban infrastructure construction was constructed from four aspects: input, output, effect, and activity. A systematic performance evaluation of urban infrastructure was conducted, and optimization suggestions were proposed accordingly.

기업이 인지한 기업지원이 위기 관리정도와 기업 성과에 미치는 영향

This study aimed to verify whether the need for business support to survive in a rapidly changing external environment affects corporate performance and whether the degree of crisis management mediates this relationship. Accordingly, a study was conducted on companies in Jeju, specifically in the intelligent tourism service and green energy solution industries, which are promoting the digital transformation of traditional sectors in response to the COVID-19 pandemic. Therefore, policy support for companies was categorized into four groups: Infrastructure, R&D, Technology support, and Business support. The impact of each support program on corporate performance was then examined through the degree of crisis management within the company. The following is a summary of the research results.
 First, the relationship between business support and crisis management was verified, showing that the impact varied depending on the characteristics of the business support program. In the short term, it was analyzed that companies require direct support programs to overcome crises. Second, the relationship between business support and corporate performance was found to be statistically insignificant. Third, in the relationship between crisis management and corporate performance, it was confirmed that managing financial and material resources in the short term can improve corporate performance. Finally, in the relationship between perceived corporate support and corporate performance, as well as the relationship between infrastructure, R&D, technology support, and corporate performance, it was found that resource management has a complete mediating effect.
 The implications of this study confirm that business support in response to uncertain business environments affects crisis management simultaneously but does not immediately impact corporate performance. As business support may have a negative effect on short-term corporate performance from the perspective of support agencies, it is necessary to establish a direction for support by dividing short-term and long-term outcomes derived through business support.

Evaluation of the contribution of the automated accounting information system to the managerial decision-making process in the commercial bank of Bangladesh

Accounting Information Systems (AIS) are becoming widespread among Bangladeshi banks. The purpose of this study is to assess how automated AIS affects Bangladeshi commercial banks. Purposeful sampling and a randomized block design were used to disseminate questionnaires to 400 respondents from Chattogram, Bangladesh, a commercial port city. Five-point Likert scale close-ended questions assess eight aspects of AIS usage and benefits: infrastructure, technological resources, understanding ability, operational comparability, relevance, reliability, decision-making, and organizational performance. Descriptive analysis, Kolmogorov–Smirnov test, Shapiro-Wilk test, factor analysis with Kaiser-Meyer-Olkin test, Cronbach's alpha, one-sample Wilcoxon Signed Rank Test, Kendall's tau-b Correlation Coefficient, Mann-Whitney test, Kruskal-WallisH test, and structural equation model were performed using MS Excel 2016, IBM SPSS Statistics 26, and IBM SPSS. The model was validated using convergent and discriminant validity tests. The study found that technological resources, operational comparability, relevance, and dependability influence AIS-based decision-making, while infrastructure and understandability do not. Private banks and AIS-trained personnel make more AIS-based decisions than public banks and non-AIS-trained workers. Regular and frequent decision-makers use AIS more than sporadic decision-makers. There is no effect of gender or job experience on AIS-based decision-making. Finally, AIS-based decision-making improved organizational performance. Therefore, the study results may assist Bangladeshi commercial banks in adopting AIS.

The estimation of infiltration and inflow in a sewage network by combining continuous monitoring of hydrological parameters, flow and temperature with stable isotopes of oxygen and hydrogen

The failure of sewage network systems can lead to the introduction of external water, impacting the capacity, performance, and environmental sustainability of urban infrastructures. This study examined methods for identifying and quantifying external water in a sewage system in cold climate conditions through the analysis of stable isotope of oxygen (δ18O) and hydrogen (δ2H) from samples, and continuous temperature monitoring, followed by the simulation of the network's hydraulics and temperature profile. The assessment was conducted during periods of low and high groundwater levels, specifically during dry weather flow. In comparison, the yearly trends of infiltration and inflow rates were assessed utilizing the moving minimum method. Using δ18O as a tracer, daily infiltration rates of 5.8 % and 35 % were estimated for periods of low and high groundwater levels, respectively. Using the outputs of the thermodynamic model, temperature was used as a tracer and the daily infiltration rates were found to be 1.5 % and 21.9 % for the same periods. The infiltration and inflow rate for the year in question was estimated to be 23 % using the moving minimum method. The findings of this study demonstrate the temporal variability of infiltration in networks and highlight the need for, as well as the potential of, a multi-faceted approach and continuous monitoring for the accurate estimation of external water before sewage network renovations are carried out.

The performance of renewable energy infrastructure

PurposeRenewable energy infrastructure is an important asset class in the context of reducing global carbon emissions going forward. This includes solar power, wind farms, hydro, battery storage and hydrogen. This paper examines the risk-adjusted performance and diversification benefits of listed renewable energy infrastructure globally over Q1:2009–Q4:2022 to examine the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio. The performance of renewable energy infrastructure is compared with the other major infrastructure sectors and other major asset classes. The strategic investment implications for institutional investors and renewable energy infrastructure in their portfolios going forward are also highlighted. This includes identifying effective pathways for renewable energy infrastructure exposure by institutional investors.Design/methodology/approachUsing quarterly total returns, the risk-adjusted performance and portfolio diversification benefits of global listed renewable energy infrastructure over Q1:2009–Q4:2022 is assessed. Asset allocation diagrams are used to assess the role of renewable energy infrastructure in a global infrastructure portfolio and in a global mixed-asset portfolio.FindingsListed renewable energy infrastructure was seen to underperform the other infrastructure sectors and other major asset classes over 2009–2022. While delivering portfolio diversification benefits, no renewable energy infrastructure was seen in the optimal infrastructure portfolio or mixed-asset portfolio. More impressive performance characteristics were seen by nonlisted infrastructure funds over this period. Practical reasons for these results are provided as well as effective pathways going forward are identified for the fuller inclusion of renewable energy infrastructure in institutional investor portfolios.Practical implicationsInstitutional investors have an important role in supporting reduced global carbon emissions via their investment mandates and asset allocations. Renewable energy infrastructure will be a key asset to assist in the delivery of this important agenda for a greener economy and addressing global warming. Based on this performance analysis, effective pathways are identified for institutional investors of different size assets under management (AUM) to access renewable energy infrastructure. This will see institutional investors embracing critical investment issues as well as environmental and social issues in their investment strategies going forward.Originality/valueThis paper is the first published empirical research analysis on the performance of renewable energy infrastructure at a global level. This research enables empirically validated, more informed and practical decision-making by institutional investors in the renewable energy infrastructure space. The ultimate aim of this paper is to articulate the potential strategic role of renewable energy infrastructure as an important infrastructure sector in the institutional real asset investment space and to identify effective pathways to achieve this renewable energy infrastructure exposure, as institutional investors focus on the strategic issues in reducing global carbon emissions in the context of increased global warming.