Infrastructure Asset Management Research Articles

An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management.

Application of Satellite-Based Remote Sensing for the Management of Pavement Infrastructure Assets

The long-term monitoring of transportation infrastructure assets at a lower cost and with short mobilization time is of significant interest to both state and federal transportation agencies in the U.S. Because of the significant improvement in spatial and temporal resolution of synthetic aperture radar (SAR) remote sensing systems and a notable reduction in the cost of data acquisition, SAR has now become a viable method to provide economic and rapid condition assessment of transportation assets. A research study was developed and performed to comprehensively perform the inspection and characterization of a pavement surface based on the amplitude of backscattering of an X-band radar. In situ characterization of the test site was first performed using traditional inertial profilers and aerial photogrammetry with unmanned aerial vehicle (UAV) surveys. The results from these in situ methods were compared with the corrected amplitude of the SAR data, which indicated that the distribution of surface roughness values computed from the inertial profiler, UAV, and SAR exhibited similar probability densities at various segmental lengths considered in this study. This suggested that the problematic areas that are evident during in situ characterization can be delineated and quantified based on the normalized radar cross section of the pavement surface. Overall, the outcome of this research exhibits the potential of SAR for future transportation asset management undertakings, and the systematic framework developed as a part of this research could be of significant interest to engineers and transportation practitioners.

Privacy-preserving culvert predictive models: A federated learning approach

In recent years, transportation agencies have shown a growing interest in the adoption of machine learning (ML) models as a means to improve the efficiency of their infrastructure asset management practices. Their limited inventory can, however, hinder the development of robust ML models. Moreover, transportation agencies may be reluctant to share their raw inventory data with others. To address these challenges, we employed a new ML paradigm, federated learning (FL), and demonstrated its potential in the case study of Utah culvert management. The Utah Department of Transportation (UDOT) only owned a limited culvert inventory, but we were able to obtain additional data from five other state DOTs’ inventories. Rather than employing a conventional centralized ML method, we opted for FL. This approach ensured that while UDOT did not directly access raw data from five other DOTs, it could still benefit from their combined insights. The FL-based model with 80.4 % accuracy showed a promising performance between the two centralized learning models developed based on the consolidated and the Utah-only datasets. The federated approach demonstrates that it is possible to develop high-performance ML models without compromising data confidentiality by centralizing data. This pioneering approach sets a precedent for other transportation agencies, suggesting they can navigate data scarcity and privacy concerns while reaping the benefits of collective knowledge.

Road transport infrastructure and supply chain asset management: evidence from manufacturing firms in the Sub-Saharan African market

Road transport infrastructure and supply chain asset management: evidence from manufacturing firms in the Sub-Saharan African market

Exploring Environmental Impacts on HVAC Infrastructure Degradation Rate

Environmental factors degrade civil infrastructure that is critical to humankind’s way of life. Sustainable asset management and capital allocation of infrastructure require an understanding of which factors most impact degradation. Heating, ventilation, and air conditioning (HVAC) system inspection records spanning 14 years from 49 locations across the USA were compiled and associated with the environmental conditions to which they were exposed. Nine environmental features were explored in this study: precipitation, minimum humidity, maximum humidity, minimum temperature, maximum temperature, wind speed, radiation, pH, and freeze–thaw cycles. Installation date, or age, was the lone nonenvironmental feature considered. Decreased precipitation, fewer freeze–thaw cycles and moderate temperatures led to lower degradation rates, while higher humidity led to higher degradation rates across the HVAC sections studied. Random forest models revealed that the most critical environmental features in predicting degradation rate were precipitation and radiation. However, feature importance varied in models that only considered subsets of the data based on either HVAC component type, initial condition of the HVAC section, or degradation rate. The results presented herein provide some insights into HVAC asset management, and the methodology used can be applied to other infrastructure systems.

Improving the Predictive Analytics of Machine-Learning Pipelines for Bridge Infrastructure Asset Management Applications: An Upstream Data Workflow to Address Data Quality Issues in the National Bridge Inventory Database

Improving the Predictive Analytics of Machine-Learning Pipelines for Bridge Infrastructure Asset Management Applications: An Upstream Data Workflow to Address Data Quality Issues in the National Bridge Inventory Database

Climate change impacts assessment on railway infrastructure in urban environments

Climate change impacts can escalate the deteriorating rate of infrastructures and impact the infrastructure's functionality, safety, operation and maintenance (O&M). This research explores climate change's influence on urban railway infrastructure. Given the geographical diversity of Sweden, the railway network is divided into different climate zones utilizing the K-means algorithm. Reliability analysis using the Cox Proportional Hazard Model is proposed to integrate meteorological parameters and operational factors to predict the degree of impacts of different climatic parameters on railway infrastructure assets. The proposed methodology is validated by selecting a number of switches and crossings (S&Cs), which are critical components in railways for changing the route, located in different urban railway stations across various climate zones in Sweden. The study explores various databases and proposes a climatic feature to identify climate-related risks of S&C assets. Furthermore, different meteorological covariates are analyzed to understand better the dependency between asset health and meteorological parameters. Infrastructure asset managers can tailor suitable climate adaptation measures based on geographical location, asset age, and other life cycle parameters by identifying vulnerable assets and determining significant covariates. Sensitivity analysis of significant covariates at one of the urban railway stations shows precipitation increment reveal considerable variation in the asset reliability.

Climate change hazards, physical infrastructure systems, and public health pathways

Climate-related hazards such as heatwaves, flooding, wildfires, and storms will increase morbidity and mortality unless infrastructure decision-makers—including urban planners, infrastructure asset managers, and utility providers—implement preventive measures to protect public health from these hazards. Existing research and policies have not systematically identified the key risk factors that these decision-makers need to manage to protect public health in a changing climate. This gap leads to unclarity regarding what infrastructure interventions are required to prevent climate-related health risks and what actors have a responsibility to manage these risks. The Climate-Health-Infrastructure-Pathways Model is introduced in this paper to address this gap and provide a conceptual map that captures the role of physical infrastructure systems in the pathways between climate-related hazards and health risks. The model surpasses what can be found in existing climate change research and policy, including the latest IPCC reporting, and is a conceptual qualitative tool that offers a typology of climate and health risks for infrastructure management. Decision-makers can use the model as a starting point to review the coverage of their current climate risk management plans and identify further opportunities to develop preventive infrastructure responses to protect public health in a changing climate.

Discovering key factors and causalities impacting bridge pile resistance using Ensemble Bayesian networks: A bridge infrastructure asset management system

Bridges are one of the critical infrastructure systems and play a critical role in supporting the economic development of nations. During the planning, design, and construction phases of bridges, it is important to ensure that the different bridge components satisfy the desired performance for avoiding catastrophic failure. One example is the foundation system with deep driven piles. A crucial consideration when planning, designing, and constructing bridge foundation system is to accurately estimate the pile resistance (i.e., capacity). However, this process constitutes a considerable challenge for bridge managers and engineers due to the uncertainties, subjectivities, and biases involved in providing accurate estimates. In fact, the current design process of bridge pile foundations is still performed in a relatively simple and deterministic manner. Thus, there is still a lack of studies that were conducted to effectively represent the causalities between different variables impacting the bridge pile resistance using objective, data-driven methods rather than subjective, expert-based methods. Therefore, this paper addresses this knowledge gap by proposing a data-driven, knowledge-discovery Bayesian network model as an expert system to (1) identify the key factors impacting the performance of bridge piles through data-driven knowledge discovery; (2) capture the causal (direct and indirect) relationships between the different variables that influence bridge pile resistance, and (3) predict bridge pile resistance in support of decision making in bridge design and construction. Multiple Bayesian network models were developed using two distinct learning paradigms based on a pile dataset with 18 variables and 2,375 data samples from a large bridge project. One paradigm is to run a regular process for learning graphical models using Bayesian structure learning, while the other one is to couple Bayesian networks with bagging ensemble learning. Model validation results showed that the best model learned by bagging ensemble learning paradigm achieves the highest prediction accuracy of 89.78% for predicting bridge pile resistance. Additionally, the developed Bayesian network models identify 8 critical factors affecting bridge pile resistance. These factors are categorized into four groups, including the external applied force on pile head, pile mechanical property (i.e., compressive stresses in bridge piles), hammer property and operation characteristics (e.g., hammer blow count), and pile cap elevation. The observations from this study would assist bridge managers and practitioners in decreasing the uncertainties that impact the estimation of pile capacity, and thus help in balancing cost-effectiveness with good performance. The paper adds to the body of knowledge by providing a great deal of insights related to the factors and causalities influencing the performance of deep bridge foundations to ultimately help in the plan, design, and construction phases of bridges.

Framework for the documentation of nature-based solutions for stormwater management

Abstract Nature-based solutions (NBSs) are widely implemented for stormwater management; as such they have become important assets that require proper asset management at different stages of their service life. Hence, there is a need for systematic documentation of the applied NBS in accordance with the principles of infrastructure asset management and in combination with a set of requirements in the newly adopted National Standard NS3456:2022 in Norway. A framework for the documentation of NBSs was developed based on a systematic literature study, the experience gained from operating NBS pilots, and the interaction with stakeholders in a research centre, Klima 2050. The framework proposed a set of specific information in the form of a data structure covering a set of categories that presents information gathered during the planning, design, operation, and maintenance phases of the NBS. Both technical and sociotechnical aspects were included in the data structure. The data structure can be tailor-made depending on the type of NBS applied. The data structure was applied in documenting the NBS pilots of the research centre to demonstrate the framework's ability to help ensure a smooth flow of information from the actors involved in the planning, constructing, and operating of the NBS.

Digitalization of bridge inventory via automated analysis of point clouds for generation of BIM models

AbstractBuilding Information Modelling (BIM) is becoming increasingly prevalent in infrastructure asset management, as it facilitates current management practices. This includes the construction of BIM models for roads, rails, bridges, tunnels etc. Bridges are particularly challenging to digitalize due to their complex geometry. The manual construction of bridge BIM models based on 2D plans is hardly feasible due to the related workload. Given the recent advancements in the field of 3D surveying and artificial intelligence, new possibilities emerge for an automated generation of as‐is bridge BIM models.This paper presents a novel, modular framework for the automatic processing of point clouds into as‐is BIM models, based on a fusion of artificial intelligence and heuristic algorithms. Representative bridge element datasets were provided to train neural network. Trained neural network can identify elements of a bridge, which are further processed using geometric algorithms into surface and solid bridge elements. This result can be additionally enriched with semantic information from existing databases. The final BIM models are exported in the standardized vendor‐free Industry Foundation Classes (IFC) format.

Barriers and opportunities towards monitoring and asset management of EU transport infrastructure

AbstractRecent technological developments have clear potential to support paradigm shift from corrective maintenance towards risk‐based/predictive maintenance through data‐informed decision‐making. The most promising developments are already taken up by local communities. Acknowledging that successful development, rollout and implementation of standardisation is only possible with thorough understanding of the full context, the H2020 CSA IM‐SAFE project evaluated the main barriers and opportunities that need to be overcome to enable well‐functioning monitoring and maintenance of transport infrastructure. As the European countries have different political, economic and social situations, the analyses have been performed on the national level, and the outcomes have been aggregated to identify main barriers at EU level. This paper presents the outcomes of the analysis of PEST barriers and SWOT analyses and discusses the connection between the barriers and the limitations in the existing EU monitoring standards and opportunities for the national implementation. Finally, the article highlights the importance to consider different scenarios and their potential environmental constraints in order to ensure that the exploitation opportunities are successful and sustainable in the long‐term.

Interoperable, platform independent structural health monitoring with long‐term availability

AbstractWithin the FFG‐project PreMainSHM the area of preventive structural health monitoring is raised to a new level of networked systems. This applies not only to the networking of self‐sustaining sensors and their sensor data, but also to the networking and utilization of relevant information for the building condition assessment and building management. The paper presents and discusses the basic considerations in terms of data models and interfaces of cloud‐based systems to guarantee interoperability between different software systems and long‐term‐availability and accessibility not only of raw but also of and relevant meta and analysed data. The starting point for further developments is provided by the existing technologies of the involved partners, which is a GIS based infrastructure asset management system and wireless as well as fibre optic sensor systems from the authors. In order to obtain information relevant to the building management, suitable analysis and prognosis procedures are discussed. Data models and analysis tools are developed and integrated into an interoperable software framework in order to provide relevant status information that can be either assessed directly by web user interfaces in form of a georeferenced digital twin or that can be used in building management systems.

Life Cycle Cost Method for Safe and Effective Infrastructure Asset Management

Existing maintenance management systems implement periodic inspections and diagnoses and perform maintenance to restore damaged facilities, making it difficult to establish a long-term and analytical budget plan. The Framework Act on Sustainable Infrastructure Management necessitates specific implementation plans for new implementation items. This study proposes a detailed method for estimating infrastructure management cost to overcome the limitations of the post-response maintenance system and establish a management plan for the Framework Act on Sustainable Infrastructure Management, considering the performance and cost effects in terms of the life cycle. The method was classified into the following stages: analysis of the performance degradation timing by deriving the performance degradation curve, analysis of proper construction methods by performance grade to establish a cost model for each member grade, representative life assessment of the establishment to determine the end-of-life of members, and analysis of optimal action timing for establishing short/mid- to long-term repair and reinforcement plans. The proposed method was applied to a water reservoir (99 reservoirs in Seoul, Korea). The performance degradation and cost prediction models for the target establishment were analyzed. The proposed method can be applied to the maintenance decision making of the management agency and is significant for efficient infrastructure maintenance.

A Deep Transfer Learning Network for Structural Condition Identification with Limited Real-World Training Data

Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data are almost always insufficient because the real-time monitoring data of a structure only reflect a limited number of structural conditions, while the number of possible structural conditions is infinite. With insufficient monitoring data, the identification performance may significantly degrade. This study aims to tackle this challenge by proposing a deep transfer learning (TL) approach for structural condition identification. It effectively integrates physics-based and data-driven methods by generating various training data based on the calibrated finite element (FE) model, pretraining a deep learning (DL) network, and transferring its embedded knowledge to the real monitoring/testing domain. Its performance is demonstrated in a challenging case, vibration-based condition identification of steel frame structures with bolted connection damage. First, disparate subsets of test data are used as training data, and the identification accuracy of the whole dataset is evaluated. The results demonstrate that the proposed approach can achieve high identification accuracy with limited types of training data, with the identification accuracy increasing up to 8.57%. Second, numerical simulation data are used as training data, and then different TL strategies and different DL architectures are compared on the performance of structural condition identification. The results show that even though the training data are from a different domain and with different types of labels, intrinsic physics can be learned through the pretraining process, and the TL results can be clearly improved, with the identification accuracy increasing from 81.8% to 89.1%. The comparative studies show that SHMnet with three convolutional layers stands out as the pretraining DL architecture, with 21.8% and 25.5% higher identification accuracy values over the other two networks, VGGNet-16 and ResNet-18. The findings of this study advance the potential application of the proposed approach towards expert-level condition identification based on limited real-world training data.

Digital transformation in municipalities for the planning, delivery, use and management of infrastructure assets: Strategic and organizational framework

As the acceleration of technological development in the built asset industry brings on waves of digital transformation (DT), traditional ways of doing and organizing are being disrupted, especially on the part of large public owners such as municipalities. For these owners, these waves of transformation require constant adaptation as they compete with existing initiatives and embedded legacy practices.This paper presents the results of the second part of a longitudinal research project aimed at framing digital transformation within municipalities to improve urban infrastructure lifecycles. More specifically, the paper presents the results of work undertaken to operationalize, extend and further validate the digital transformation framework that has been developed in part 1 and which is presented elsewhere. The theoretical framework acts as a guide and analysis tool for the digital transformation of municipalities and aims to help them reduce and/or eliminate the barriers and challenges in this digital transformation. To do so, the results from a survey conducted within 44 municipalities and interviews conducted with 13 municipalities of different sizes are presented and discussed through the theoretical framework.The results show that data and information management remain the key issues, especially in a siloed organizational context such as those found within municipalities. Moreover, a significant amount of organizations remain unaware of how to approach digital transformation which in turn leads to disinterest or disengagement in digital transformation, which results in localized or fragmented initiatives. This in turn can cause delays in implementing transformational initiatives and contributes to maintaining a low level of digital maturity. The study also highlights the critical lack of human resources, expertise and appropriate training to support digital transformation.

Water infrastructure asset management: state of the art and emerging research themes

The long-term maintenance of infrastructure requires asset management. This latter is defined as a series of actions aimed at the long-term maintenance of the capacity of an infrastructure to provide an efficient service for users, at costs and with impacts that are tolerable for society and the environment. The actions implemented concern notably the inventory of infrastructures, their monitoring, inspection and maintenance, the analysis of their state and performance, their reinforcement, renovation, and the definition of long-term technical-financial policies. This article is structured around four themes based on the authors’ experience faced with an analysis of the perspectives set out in recent articles. For each theme, a state of the art largely based on the literature in the field, as well as emerging and nevertheless pressing needs in terms of knowledge production and tools to support reflection and decision-making are presented: temporal and spatial dimensions of water infrastructure asset management and their consistency; multi-infrastructure management seen from the perspective of physical interdependences and decision-aids; governance, organisations and territories; digital representation of the socio-technical infrastructural system. The analysis is carried out following several disciplines from the engineering and, the human and social sciences. Infrastructure linked to water are studied.

Multiagent Reinforcement Learning for Project-Level Intervention Planning under Multiple Uncertainties

Reinforcement learning (RL) has recently been adopted by infrastructure asset management (IAM) researchers for adding flexibility regarding uncertainties in preventive actions decision-making. However, this relatively recent line of research has not incorporated other sources of uncertainties, such as hazards apart from deterioration patterns, nor has such research considered managerial aspects of IAM, such as stakeholders’ utilities. This paper aims to provide a holistic framework that draws upon recent developments in IAM systems and microworlds, employs RL model training, and considers deterioration, hazards, and cost fluctuations as the main sources of uncertainties while also adopting managerial aspects into decision-making. Consistent with the existing practice of IAM, this framework brings flexibility in the face of uncertainties to the IAM decision-making process. Multi-agent RL models based on deep Q networks and actor-critic models are constructed and trained for taking intervention actions regarding elements of a real bridge in Indiana through its life cycle. Both models could lead to higher expected utilities and lower costs compared to the optimal maintenance, rehabilitation, and reconstruction (MRR) plans obtained by Monte Carlo simulation and heuristic optimization algorithms. The proposed framework can assist decision-making bodies and managers in the IAM domain with making updateable optimal and more realistic decisions based on the updated state of various complex uncertainties in a negligible amount of time.

Integration of TLS-derived Bridge Information Modeling (BrIM) with a Decision Support System (DSS) for digital twinning and asset management of bridge infrastructures

In the current modern era of information and technology, the concept of Building Information Modeling (BIM), has made revolutionary changes in different aspects of engineering design, construction, monitoring, and management of infrastructure assets, especially bridges. In the field of bridge engineering, Bridge Information Modeling (BrIM), as a specific form of BIM, includes digital twinning of bridge assets associated with geometrical information and non-geometrical inspection data. BrIM has demonstrated tremendous potential in substituting traditional paper-based documentation and hand-written reports with digital bridge documentation/transformation, allowing professionals and managers to execute bridge management more efficiently and effectively. However, concerns remain about the quality of the acquired data in BrIM development, as well as lack of research on utilizing these information for remedial actions/decisions in a reliable Bridge Management System (BMS), which are mainly reliant on the knowledge and experience of the involved inspectors, or asset managers, and are susceptible to a certain degree of subjectivity. To address these concerns, this research paper presents a comprehensive methodology as an advanced asset management system that employs BrIM data to improve and facilitate the BMS. This innovative BMS is comprised of a precise Terrestrial Laser Scan (TLS)-derived BrIM as a qualitative digital replica of the existing bridge, incorporating geometrical and non-geometrical information of the bridge elements, and equipped with a requirement-driven framework in a redeveloped condition assessment model for priority ranking of bridge elements based on their health condition. In another step ahead, the proposed BMS integrates a Decision Support System (DSS) to score the feasible remedial strategies and provide more objective decisions for optimal budget allocation and remedial planning. This methodology was further implemented via a developed BrIM-oriented BMS plugin and validated through a real case study on the Werrington Bridge, a cable-stayed bridge in New South Wales, Australia. The finding of this research confirms the reliability of BrIM-oriented BMS implementation and the integration of proposed DSS for priority ranking of bridge elements that require more attention based on their structural importance and material vulnerability, as well as optimizing remedial actions in a practical way while preserving the bridge in a safe and healthy condition.

Multi-objective optimization for the sustainability of infrastructure projects under the influence of climate change

ABSTRACT Infrastructure asset management is concerned with the efficient and sustainable utilization of resources. There are numerous sources of uncertainties associated with the physical state of the infrastructure, climate change, and the economy. Thus, the most appropriate decision-making process to select maintenance and replacement strategies that are sustainable, economical, and safe should also be well informed in terms of risks. The decision process is formulated as a multi-objective optimization problem and exemplified the case for targeted performance levels and total costs, and solved using both Multi-Objective Particle Swarm Optimization (MOPSO) and a Non-dominated Sorting Genetic Algorithm II (NSGA-II). The results indicate that annual budget constraints have a significant effect on the Pareto front and the schedule associated with individual solutions. The proposed approach provides a useful and flexible decision-analysis tool for managers by allowing for multiple objective optimization in scheduling future interventions towards the sustainability of infrastructure.