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This paper aims to investigate the moisture diffusion behavior in a system-in-package module systematically by moisture-thermalmechanical-coupled finite element modeling with different structure parameters under increasingly harsh environment.

A finite element model for a system-in-package module was built with moisture-thermal-mechanical-coupled effects to study the subsequences of hygrothermal conditions.

It was found in this paper that the moisture diffusion path was mainly dominated by hygrothermal conditions, though structure parameters can affect the moisture distribution. At lower temperatures (30°C~85°C), the direction of moisture diffusion was from the periphery to the center of the module, which was commonly found in simulations and literatures. However, at relatively higher temperatures (125°C~220°C), the diffusion was from printed circuit board (PCB) to EMC due to the concentration gradient from PCB to EMC across the EMC/PCB interface. It was also found that there exists a critical thickness for EMC and PCB during the moisture diffusion. When the thickness of EMC or PCB increased to a certain value, the diffusion of moisture reached a stable state, and the concentration on the die surface in the packaging module hardly changed. A quantified correlation between the moisture diffusion coefficient and the critical thickness was then proposed for structure parameter optimization in the design of system-in-package module.

The different moisture diffusion behaviors at low and high temperatures have seldom been reported before. This work can facilitate the understanding of moisture diffusion within a package and offer some methods about minimizing its effect by design optimization.

The building sector contributes one-third of the energy-related carbon dioxide globally. Therefore, framing appropriate energy-related policies for the next decades becomes essential in this scenario to realize the global net-zero goals. The purpose of the proposed study is to evaluate the impact of the widespread adoption of such guidelines in a building community in the context of mixed-mode buildings.

This study decentralizes the theme of improving the energy efficiency of the national building stock in parcels by proposing a community-based hybrid bottom-up modelling approach using urban building energy modelling (UBEM) techniques to analyze the effectiveness of the community-wide implementation of energy conservation guidelines.

In this study, the UBEM is developed and validated for the 14-building residential community in Mumbai, India, adopting the framework. Employing Energy Conservation Building Code (ECBC) compliance on the UBEM shows an energy use reduction potential of up to 15%. The results also reveal that ECBC compliance is more advantageous considering the effects of climate change.

In developing countries where the availability of existing building stock information is minimal, the proposed study formulates a holistic framework for developing a detailed UBEM for the residential building stock from scratch. A unique method of assessing the actual cooling load of the developed UBEM is presented. A thorough sensitivity analysis approach to investigate the effect of cooling space fraction on the energy consumption of the building stock is presented, which would assist in choosing the appropriate retrofit strategies. The proposed study's outcomes can significantly transform the formulation and validation of appropriate energy policies.

This article introduces the Special Issue on Sustainable Management of Heritage Buildings in long-term perspective.

It starts by reviewing the gaps in knowledge and practice which led to the creation and implementation of the research project SyMBoL—Sustainable Management of Heritage Buildings in long-term perspective funded by the Norwegian Research Council over the 2018–2022 period. The SyMBoL project is the motivation at the base of this special issue.

The editorial paper briefly presents the main outcomes of SyMBoL. It then reviews the contributions to the Special Issue, focussing on the connection or differentiation with SyMBoL and on multidisciplinary findings that address some of the initial referred gaps.

The article shortly summarizes topics related to sustainable preservation of heritage buildings in time of reduced resources, energy crisis and impacts of natural hazards and global warming. Finally, it highlights future research directions targeted to overcome, or partially mitigate, the above-mentioned challenges, for example, taking advantage of no sestructive techniques interoperability, heritage building information modelling and digital twin models, and machine learning and risk assessment algorithms.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

Living walls (LWs), vegetated walls with an integrated growth layer behind, are being increasingly incorporated in buildings. Examining plant characteristics’ comparative impacts on LWs’ energy efficiency-related thermal behavior was aimed, considering that studies on their relative effects are limited. LWs of varying leaf albedo, leaf transmittance and leaf area index (LAI) were studied for Antalya, Turkey for typical days of four seasons.

Dynamic simulations run by Envi-met were used to assess the plant characteristics’ influence on seasonal and orientation-based heat fluxes. After model calibration, a sensitivity analysis was conducted through 112 simulations. The minimum, mean and maximum values were investigated for each plant characteristic. Energy need (regardless of orientation), temperature and heat flux results were compared among different scenarios, including a building without LW, to evaluate energy efficiency and variables’ impacts.

LWs reduced annual energy consumption in Antalya, despite increasing energy needs in winter. South and west facades were particularly advantageous for energy efficiency. The impacts of leaf albedo and transmittance were more significant (44–46%) than LAI (10%) in determining LWs’ effectiveness. The changes in plant characteristics changed the energy needs up to ca 1%.

This study can potentially contribute to generating guiding principles for architects considering LW use in their designs in hot-humid climates.

The plant characteristics’ relative impacts on energy efficiency, which cannot be easily determined by experimental studies, were examined using parametric simulation results regarding three plant characteristics.

This research aims to study the materials that compose older reinforced concrete bridges which are damaged and degrading to explain the mechanisms and origins of various disorders. Therefore, this work will contribute to providing answers on the capacity of nondestructive evaluation method during the diagnosis. In addition to the characterization of affected structures, it will aim to provide effective solutions for different serious pathologies.

In this context, two bridges located on NH16 and NH21, respectively, were studied in Annaba city (north-east Algeria), specifically in El-Hadjar municipality located in the central industrial zone of Pont-Bouchet. This study makes it possible to make conclusions from the in-depth diagnosis based on disorders exposition causes and mechanical characteristics evolution by non-destructive testing (NDT) tools. Furthermore, solutions are proposed, including conservation maintenance of these degraded structures.

All degradations can be the result of several factors: either human (poor design) or chemical (surface water, wastewater and groundwater quality (acidic or basic)). In addition to other natural causes (geological formations, flood phenomena or climate), NDT tools play a major role in the evaluating mechanical performance of degraded structures (resistance and hardness).

The NDT techniques can be transmitted to civil engineering experts because their training is limited regarding mechanical and structural construction.

NDT tools are the most suitable for in-situ assessing, and the concrete constructions health state, so far from financial problems.

Degraded bridge diagnosis by NDT testing is necessary for a thorough safety evaluation (mechanical performance, strength and deformability), to protect human lives and design durability.

This is an original paper which contains new information at different scales and from special fields, based on an evaluation using NDT tools on real degraded structures. It can be used to improve the knowledge of materials employed in a bridge without performing expensive direct tests or the need for destroying it.

The purpose of this study is to ensure the safe use of carbon fiber composite pressure vessels in the nuclear industry environment.

This study investigated the degradation behaviors of carbon fiber reinforced composite (CFRP) using the specific corrosive media HF solution, with a focus on the damage to the surface epoxy layer. The degradation behaviors of CFRP in HF solution were examined by electrochemical methods and surface characterization, using HCl, NaCl and NaF solution for comparison.

The results showed that the specimen in HF solution will have a value of |Z|_0.01 Hz one order of magnitude lower, a substantially lower contact angle, more breakage of the surface epoxy and the stronger O─H peak and weaker C─O─C peak in the Fourier transform infrared spectrum, indicating severe hydrolytic damage to the surface epoxy.

The work focuses on the degradation damage to CFRP surface epoxy by specific corrosive media HF.

The purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).

The approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.

The findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.

This review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

Information and communication technology (ICT) implementation has demonstrated usefulness in supply chain coordination and efficiency optimization in various industries and sectors. This study investigates the extent of ICT deployment in fruits and vegetable supply chains (FVSC) from “farm-to-fork” to ensure food security.

This paper employs a systematic literature review (SLR) methodology and identified a total of 99 journal articles ranging from 2001 to April 2023 for analysis. The reviewed articles have been classified based on the framework proposed from the perspective of food security. Bibliometric and content analysis is carried out with the final list of articles to extract useful insights.

The findings reveal that ICT implementation in FVSC is a relatively new research area; researchers have started investigating several aspects of ICT in FVSC through varied research methodologies. Experimental research aimed at addressing food safety and condition monitoring of fruits and vegetables (FV) has started to gain traction while theory building is yet to gain traction in the literature reviewed. Findings indicate further research is required on technologies like blockchain (BCT), artificial intelligence (AI) and machine learning (ML), especially on key objectives such as food security, and the triple-bottom-line approach of sustainability. It also indicates that implementing relevant ICTs in FVSC can help delay, if not avert, the food crisis predicted by Malthusian theory.

This study used only well-established databases to ensure quality of the studies examined. There is a possibility of missing out on articles from other sources not considered. As a result, future SLR studies may employ additional databases, such as Springer Link, Taylor and Francis, Emerald Insight and Google Scholar. Other methodologies such as expert interviews and extra empirical methodologies may also be employed to give a more balanced picture and insights into ICTs implementation in FVSC.

This study offers a summative detail of the status of ICT implementation in FVSC and can serve as a reference guide for stakeholders in developing strategies for efficient FVSC management. This research work highlights the impact of ICT implementation in FVSC on the four pillars of food security which include improved availability, accessibility, utilization and stability.

This study focuses on ICT implementation for food security in FVSC. The SLR highlights the gaps and proffers potential solutions that enhance global efforts on food security through ICT-enabled reduction in food waste and food loss in FVSC.

Several factors influence the costs of buildings. Thus, identifying the cost significant factors can assist to improve the accuracy of project cost forecasts during the planning phase. This paper aims to identify the cost significant parameters and explore the potential for improving the accuracy of cost forecasts for buildings using machine learning techniques and large data sets.

The Australian State of Victoria Building Authority data sets, which comprise various parameters such as cost of the buildings, materials used, gross floor areas (GFA) and type of buildings, have been used. Five different machine learning regression models, such as decision tree, linear regression, random forest, gradient boosting and k-nearest neighbor were used.

The findings of the study showed that among the chosen models, linear regression provided the worst outcome (r² = 0.38) while decision tree (r² = 0.66) and gradient boosting (r² = 0.62) provided the best outcome. Among the analyzed features, the class of buildings explained about 34% of the variations, followed by GFA and walls, which both accounted for 26% of the variations.

The output of this research can provide important information regarding the factors that have major impacts on the costs of buildings in the Australian construction industry. The study revealed that the cost of buildings is highly influenced by their classes.