In response to slow information updates, low data consistency, and long system response times in the operation and maintenance of super large bridges, this study proposes a CATIA-based bridge modelling and management system. The system adopts a bottom-up design framework and integrates parametric family files, automated assembly, and CATIA-Excel-Python linkage to support rapid model creation and real-time defect updates. Performance tests show that the system achieves superior efficiency compared with existing platforms, with a modelling speed of 1.5 models per hour, an information update delay of 5 min, data consistency of 98.7%, and a system response time of 0.5 s. A six-year engineering application on a major bridge in Shanxi Province further verifies the system’s advantages: operation and maintenance costs rose moderately from 1.5 to 2.3 million yuan, representing nearly 40% savings compared with conventional systems whose costs increased from 2.4 to 4.1 million yuan. In addition, the proposed system achieved higher scores in safety, cost-effectiveness, and robustness (85, 78, and 82 out of 100, respectively). Overall, the CATIA-based system effectively enhances data processing efficiency, reduces long-term maintenance costs, improves structural safety, and offers a practical and intelligent solution for full-lifecycle management of super large bridges.

Decision-support tools are fundamental to planning road and highway infrastructure maintenance, guiding treatment interventions and their timing. Despite the development of numerous complex decision-support tools, public sector agencies frequently hesitate to adopt them. This paper presents a metamodel framework designed to bridge this gap by matching transportation asset management decision-support tools to specific decision-making contexts or scenarios, thereby promoting use of the most appropriate tools. The framework uses machine learning methods to characterise scenarios and select the most suitable model. The framework is applied to three distinct decision-support tools to determine maintenance policies for pavements and bridges: a comprehensive tool that minimises total costs (agency, user, and disruption) over a planning horizon using Monte Carlo simulation (in which random sampling is used to represent different condition states) and network equilibrium, a simplified fixed policy tool, and a tool of intermediate complexity. Trained and validated using pseudo-data from 400 scenarios across 10 simulated networks, and further tested on a realistic network, the metamodel classifier accurately predicts the most preferred decision-support tool for previously unseen scenarios.

This work investigates the potential for improved accuracy of a cost-estimating method called weighted reference class forecasting (WRCF) with respect to the traditional reference class forecasting (RCF) method. WRCF is a modification of the RCF method, which uses historical project cost data to produce project cost estimate uplifts. WRCF uses work package-level cost deviations to overcome a limitation of RCF: the unavailability of reference data. However, WRCF has not been validated, raising concerns about the method’s reliability. This case study uses real project data from a utility company in simulated project portfolios and cross-validation to assess the validity of WRCF. The study contributes to the robustness of RCF in practice by providing empirical evidence on the performance of WRCF. Results indicate that WRCF produces more conservative cost estimates than the traditional RCF. In addition, WRCF cost estimates have a higher level of certainty than expected, potentially leading to inflated cost estimates beyond what is intended. This study emphasises the importance of practitioners selecting appropriate WRCF percentiles to avoid excessive conservatism in cost estimates and overallocation of resources. Further research is needed to assess WRCF’s validity across diverse industries and regions, providing insights for practitioners in estimating project cost uplifts.

As a non-renewable cultural heritage, grotto temples face many value threats and the crises of rapid extinction. Due to the unique environment, diverse volume and complex geometric structure of grotto temples, cultural relic protection faces huge challenges. Digitisation has become the most important and effective preventive protection method. This study proposes a comprehensive modeling method, which uses three-dimensional (3D) laser scanning technology to obtain the point cloud data of grotto ruins, close range photogrammetry to obtain multi-angle and high-resolution two-dimensional (2D) image data, and through the fusion modelling of the laser point cloud and image dense matching point cloud, a complete geometric model of grotto is obtained. The registration method of the improved algorithm is based onEuclidean distance residual. The performance is better, with residuals mainly distributed in the range of 0.02 to 0.03mm. In terms of texture mapping, the accuracy of model mapping is adjusted by adjusting the constraints of control points. The gray values of 50 pixels around the control points show significant gradient changes in the X direction, while the changes in the Y direction are relatively small. These methods and technologies provide practical and effective means for the protection and preservation of cultural relics in grotto temples.

Bridge managers need to generate a complete overview of required interventions, possession windows and likely costs 10–20 years ahead of execution. These, even if approximate, help ensure stable train schedules. With the increasing amount of available data and the increasing desire to exploit digitalisation to improve decision making, bridge managers are perfectly poised to make or improve these estimates by moving on from current qualitative methods. This paper proposes a way in the current climate to use digitalisation and existing data to generate approximate overviews of required interventions 10–20 years ahead of time, including estimates of component-level bridge interventions, possession windows, likely costs and the increases in failure risks if interventions are postponed. A demonstration is done on 41 bridges of a 25 km railway network in Switzerland. It is argued that the algorithm generates a more complete and consistent overview of component-level interventions, possession windows and costs compared with current qualitative methods. Additionally, the algorithm generates a solid basis for the initiation of detailed investigations of the bridges by engineering offices – that is, the investigations that result in the information required for scheduling an intervention, as well as estimating the type of intervention and track possession that are required.

Developing countries, such as South Africa, have no choice but to look at innovative and/or alternative approaches, such as public–private partnerships (PPPs), as investment models, to ensure that they eliminate their water infrastructure backlogs. The primary objectives of this research were (a) to develop a PPP framework; (b) to identify PPP investment models for water infrastructure; and (c) to determine key categories, criteria and characteristics for cost-effective PPP investment models to ensure the sustainability of the water infrastructure value chain in South Africa. The framework for PPPs identified five broad categories of investment models for the water infrastructure value chain in South Africa. The research results highlight the aspects of PPPs in addressing (a) water infrastructure needs, (b) an implementation strategy for water infrastructure projects, (c) investment policies and (d) the eradication of water infrastructure backlogs. The total investment estimates in the past 20-year period (1998/1999–2019/2020) have been about US$48.36 billion, with the public sector contributing about US$44.32 billion (91.64%) and the private sector only US$4.04 billion (8.36%). Furthermore, the analysis showed that about US$2.75 billion/annum is available based on the current financial arrangements, leaving a financial gap of US$2.83 billion/annum in the water infrastructure value chain.

Organisations manage assets to provide services to various customers effectively and efficiently through the development of a comprehensive asset management system. The asset management strategy is a core component of an asset management system that is defined from a different perspective in the area of asset management. An explicit definition will make it easy to comprehend, develop and implement. The development and implementation of an effective asset management strategy play a vital role in formulating a long-term capital works programme as part of capital investment planning. How to develop and implement the strategy to create an effective capital works programme is the main research question. This paper defines the asset management strategy at three levels and develops and implements the strategy to create the capital works programme for roads using a case study approach following a six-step methodology.

A significant portion of railway network income is spent on the maintenance and restoration of the railway infrastructure to ensure that the networks continue to provide the expected level of service. The execution of the interventions – that is, when and where to perform maintenance or restoration activities, depends on how the state of the infrastructure assets changes over time. Such information helps ensure that appropriate interventions are selected to reduce the deterioration speed and to maximise the effect of the expenditure on monitoring, maintenance, repair and renewal of the assets. Presently, there is an explosion of effort in the investigation and use of data-driven methods to estimate deterioration curves. However, real-world time history data normally includes measurement of errors and discrepancies that should not be neglected. These errors include missing information, discrepancies in input data and changes in the condition rating scheme. This paper provides solutions for addressing these issues using machine learning algorithms, estimates the deterioration curves for railway supporting structures using Markov models and discusses the results.

Cyclones and the associated floods have recently become a regular problem for many states of India, particularly on its west and east coasts. Odisha (renamed from Orissa in 2011) is a state located along the eastern coast of India that experiences tropical cyclones mainly from the Bay of Bengal. Some of the recent severe cyclonic storms that caused large-scale devastation in Odisha in the last decade include cyclone Amphan in May 2020, cyclone Fani in May 2019, cyclone Titli in October 2018, cyclone Hudhud in October 2014 and cyclone Phailin in October 2013. The roads, being one of the essential lifeline infrastructure facilities, commonly get damaged during these cyclonic floods in terms of floodwater overtopping, erosion of road surfaces, shoulders and embankment slopes, and even the washing out of the whole roadway section. These recent events have indicated that special attention is needed to minimise damage to this vital lifeline infrastructure by developing and adopting climate-resilient road infrastructure. This paper focuses on the damage assessment of transportation infrastructure during the recent cyclones and associated floods in Odisha, and advances some recommendations for possible measures to be taken for the design of cyclone- and flood-resilient road infrastructure.