The concept of digital twins (DT)s enhances traditional structural health monitoring (SHM) by integrating real-time data with digital models for predictive maintenance and decision-making whilst combined with finite element modelling (FEM). However, the computational demand of FE modelling necessitates surrogate models for real-time performance, alongside the requirement of inverse structural analysis to infer overall behaviour via the measured structural response of a structure. A FEM-based machine learning (ML) model is an ideal option in this context, as it can be trained to perform those calculations instantly based on FE-based training data. However, the performance of the surrogate model depends on the ML model architecture. In this light, the current study investigates three distinct ML models to surrogate FE modelling for DTs. It was identified that all models demonstrated a strong performance, with the tree-based models outperforming the performance of the neural network (NN) model. The highest accuracy of the surrogate model was identified in the random forest (RF) model with an error of 0.000350, whilst the lowest inference time was observed with the trained XGBoost algorithm, which was at approximately 1 millisecond. By leveraging the capabilities of ML, FEM, and DTs, this study presents an ideal solution for implementing real-time DTs to advance the functionalities of current SHM systems.

This study introduces a quantitative approach to evaluating the resilience of oil pipeline systems against various natural and physical disruptions. Resilience is increasingly essential in critical infrastructure to ensure continuous operations and minimize disruption impacts. However, existing quantitative methods often need specific time-dependent data, making measuring resilience in pipeline infrastructure challenging. To address this gap, this paper proposed a comprehensive framework by integrating the existing incident database with key features of assessing failure probabilities based on historical events and developing multi-event resilience indicators based on system performance under various disruptions. The methodology employs event tree analysis to quantify the probabilities of multiple failure scenarios and their impact on pipeline operations and recovery efforts. The practical application of the proposed approach was demonstrated using real-world oil pipeline incident data from across the United States, covering the period from 2010 to 2022. The focus was on multiple event scenarios involving pipeline disruptions, followed by shutdowns, examining how these events collectively impact pipeline resilience. The results indicate that corrosion failure, equipment failure, and natural hazard damage significantly impact oil pipeline resilience. Corrosion and equipment failures affect resilience primarily due to their frequency, while natural hazard damage, despite its lower occurrence rate, is more unpredictable and often requires more frequent shutdowns. Understanding these failure causes and their impacts is essential for enhancing the resilience and sustainable operation of oil pipeline systems.

A fresh paradigm for classifying current studies on flood management systems is proposed in this review. The literature has examined methods for managing different flood management activities from a variety of fields, such as machine learning, image processing, data analysis, and remote sensing. Prediction, detection, mapping, evacuation, and relief efforts are all part of flood management. This can be improved by adopting state-of-the-art tools and technology. Preventing floods and ensuring a prompt response after floods is crucial to ensuring the lowest number of fatalities as well as minimizing environmental and financial damages. The following noteworthy research questions are addressed by the framework: (1) What are the main methods used in flood control? (2) Which stages of flood management are the majority of research currently in existence focused on? (3) Which systems are being suggested to address issues with flood control? (4) In the literature, what are the research gaps regarding the use of technology for flood management? To classify the many technologies that have been studied, a framework for classification has been provided for flood management. It was found that there were few hybrid models for flood control that combined machine learning and image processing. Furthermore, it was discovered that there was little use of machine learning-based techniques in the aftermath of a disaster. To provide efficient and comprehensive disaster management, future efforts must concentrate on integrating image processing methods, machine learning technologies, and the understanding of disaster management across all phases. The study has proposed the use of Generative Artificial Intelligence.

Polystyrene is renowned for its excellent thermal insulation due to its closed-cell structure that traps air and reduces heat conduction. This study aims to develop sustainable, energy-efficient building materials by enhancing the thermal and mechanical properties of plaster–polystyrene bio-composites. By incorporating varying amounts of polystyrene (5% to 25%) into plaster, our research investigates changes in thermal conductivity, thermal resistance, and mechanical properties such as Young’s modulus and maximum stress. Meticulous preparation of composite samples ensures consistency, with thermal and mechanical properties assessed using a thermal chamber and four-point bending and tensile tests. The results show that increasing the polystyrene content significantly improved thermal insulation and stiffness, though maximum stress decreased, indicating a trade-off between insulation and mechanical strength.

Styrene–butadiene–styrene (SBS)-modified asphalt is widely used in the field of road construction because it helps asphalt pavements achieve good road performance. However, SBS-modified asphalt has problems of poor compatibility, leading to insufficient thermal storage stability. As a block copolymer of styrene and butadiene, the compatibility of SBS with asphalt is also influenced by its styrene-to-butadiene (S/B) ratios. To reveal the compatibility mechanisms of different S/B ratios of SBS and asphalt during system stabilization, the interactions of SBS with asphalt at the molecular level were investigated in this study. Based on the molecular dynamics simulation method, interfacial models of SBS and asphalt were constructed; the miscible process of SBS in asphalt was simulated, with the characteristics of phase structure evolution and molecular distribution being analyzed; and the binding energy of the SBS/asphalt miscible systems was calculated. The results show that a higher butadiene content benefits the miscibility of SBS in asphalt and that the S/B ratios affect the interaction of SBS with asphalt and its components. SBS with a 3:7 ratio of styrene to butadiene exhibits stronger adsorption with the resin component and has the highest binding energy and best compatibility with asphalt. The findings contribute to the understanding of the miscibility and compatibility mechanisms between different S/B ratios of SBS and asphalt.

The ever-increasing material performance requirements in modern engineering structures have thrust engineered cementitious composites (ECCs) into the limelight of civil engineering research. The exceptional tensile, bending, and crack-control abilities of ECCs have sparked significant interest. However, the current research on the mechanical behavior of ECCs primarily focuses on uniaxial tensile and compressive constitutive relationships, leaving a gap in the form of a comprehensive multidimensional constitutive model that can fully describe its complex behavior at large strains. This study rigorously addresses this gap by initially investigating the uniaxial tensile and compressive behavior of ECCs through experiments and establishing a one-dimensional constitutive relationship of ECCs. It then introduces the concepts of damage energy release rate and energy equivalent strain, and constructs a three-dimensional constitutive model of ECCs by introducing the damage variable function. We write the numerical algorithm of our theoretical model in terms of the VUMAT subroutine and implement it into ABAQUS 2019 finite element software. We validate the accuracy and practicality of the multidimensional constitutive model by comparing the experimental data of uniaxial tension/compression and four-point bending. This paper enriches the theoretical system of ECCs and provides rigorous guidance for the performance optimization and practical application of such advanced engineering materials.

We develop a new analytical and numerical approach, based on existing models, to describe the onset of lateral–torsional buckling (LTB) for simply supported thin-walled steel members. The profiles have uniform I cross-sections with variable lengths of the flanges, to describe also H cross-sections, they are prestressed by external tendons, and they are subjected to fire and various loadings. Our approach manages to update the value of the prestressing force, accounting for thermal and loads; the critical multipliers result from an eigenvalue problem obtained applying Galërkin’s approach to a system of nonlinear equilibrium equations. Our results are compared to buckling, steady state, and transient state analyses of a Finite Element Method (FEM) simulation, in which an original expression for an equivalent thermal expansion coefficient for the beam–tendon system that accounts for both mechanical and thermal strains is introduced. Our aim is to find estimates for the critical conditions with no geometric imperfections and accounting for the decay of material properties due to fire, thus providing limit values useful for conservative design. This approach can surpass others in the literature and in the existing technical norms.

The aim of this work is to analyse the quality and transparency of data on the additional costs of public works. The problem identified is the lack of detailed and accessible information that allows for an adequate analysis of the performance and final state of public works, especially in relation to prices and deadlines. This is a case study carried out in Portugal, in which information from public works contracts with a closing date in 2022 was analysed. The data were extracted from the public access portal, responsible for making available and publishing information on the execution of public works contracts. The information was subjected to a statistical treatment process seeking to identify answers to transparency issues. The originality of this study lies in the quantitative and statistical approach applied to the evaluation of the transparency of the data made available on the portal, contributing to the debate on improving public management. The results indicate the need to expand the content available on the portal since the information provided does not allow for an analysis of the final state and performance of the works carried out, especially those relating to price and deadline, which in turn limits the construction of forecasting models and performance indicators. Corrective measures are proposed that include information that allows for answering questions about transparency and that allows for the construction and analysis of statistics and indicators, contributing to identifying the need for improvements in legislation, and the adoption of mechanisms that can improve, correct and or reinforce actions with an impact on the management of public resources.

This study addresses critical risk factors in high-voltage power transmission line (HVPTL) construction projects, which are vital components of national energy infrastructure. HVPTL projects are essential for meeting energy needs but are often plagued by risks due to their linear construction nature, leading to project underperformance. However, the lack of attention to risk management often leads to project underperformance. This research aims to identify and rank these risks to facilitate effective risk management. Through literature review and preliminary surveys, 63 risk elements were identified under 14 main categories. These risks were ranked using two rounds of Delphi surveys and the analytical hierarchy process (AHP). The study focuses on a Sri Lankan HVPTL project. The most critical risk factors identified include “improper planning by the main contractor”, “delays in decision-making by the client/consultant”, “errors in initial costing”, and “inaccuracies in survey data”, with AHP analysis assigning significant weights of 43.9%, 18%, 16%, and 14.9% to these factors, respectively. Comparative analysis with similar studies reveals consistent findings, underscoring the importance of addressing delays in approvals, material unavailability, and construction-quality challenges. These results emphasize the necessity of adopting systematic risk-management techniques in HVPTL projects to mitigate uncertainties and enhance project outcomes.

This research used the SWARA approach to analyze risk assessment criteria for public–private partnership (PPP) projects in Iran’s water and sewage sectors to identify and prioritize the most significant elements influencing project success from public and private viewpoints. Key results show that the public sector considers “risk probability” to be the most important aspect, highlighting the requirement for stability and predictability in project outcomes. In contrast, the private sector prioritizes the “ability to predict and discover risk”, emphasizing efficiently anticipating and managing uncertainty. Furthermore, this study revealed five common major risk characteristics, including “risk manageability” and “uncertainty of risk”; however, their rankings differ per industry, demonstrating various risk prioritizing methodologies. This study is unique in that it focuses only on Iran’s water and sewage infrastructure, an area historically neglected in PPP research, providing a rare investigation of sector-specific hazards as well as the interaction between public and private interests in a developing country environment. The paper makes specific suggestions, calling for more openness, improved communication, and the use of sophisticated risk management techniques to bridge the gap across sectors. These findings not only add to the scholarly knowledge of PPP dynamics in emerging countries but also provide practical recommendations for governments and private investors navigating Iran’s infrastructure issues.