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Based on the known positive effects of conventional hot-dip galvanizing under fire exposure and indicative results on zinc–aluminum coatings from smallscale tests, a series of tests were conducted on zinc-5% aluminum galvanized test specimens under fire loads to verify the previous positive findings under largescale boundary conditions.

The emissivity of zinc-5% aluminum galvanized surfaces applied to steel specimens was determined experimentally under real fire loads and laboratory thermal loads in accordance with the normative specifications of the standard fire curve. Both large and smallscale specimens were used in this study. The steel grade and surface conditions of the specimens were varied for both test scenarios.

Largescale tests on specimens with typical steel construction dimensions under fire loads showed that the surface emissivity of zinc-5% aluminum galvanized steel was significantly lower than that of the conventionally galvanized steel. Only minor influences from the weathering of the specimens and steel chemistry were observed. These results agree well with those obtained from smallscale tests. The design values of zinc-5% aluminum melt (Zn5Al) required for the structural fire design were proposed based on the obtained results.

The novel tests presented in this study are the first ones to study the behavior of zinc-5% aluminum galvanized largescale steel construction components under the influence of real fire exposure and their positive effect on the emissivity of steel components galvanized by this method. The results provide valuable insights and information on the behavior in the case of fire and the associated savings potential for steel construction.

This paper provides a summary of the issues in the passive fire protection of steel structures. Types of passive fire protection and the material properties of protection members and steel members are described. The paper deals with the possibility of partial fire protection for secondary steel beams, in cases where, due to possible membrane action, it is not necessary to apply passive protection to the entire beams.

Studies of partially fire-protected steel structures are compared, and results from studies with different input data are summarized. A fire experiment was conducted to investigate the effect of partial passive protection in a small-scale furnace. Based on the findings of the experiment, numerical models were prepared using Ansys Mechanical.

The results are summarized, and a partial fire protection length of 500 mm is recommended. Various partial fire protection lengths were compared, and the temperature development of the steel contactors was compared using a protection length of 500 mm. At the end of the paper, options for partial passive protection of steel beams are presented.

Extended paper from ASFE2021 based on selection.

Intumescent coatings are nowadays a dominant passive system used to protect structural materials in case of fire. Due to their reactive swelling behaviour, intumescent coatings are particularly complex materials to be modelled and predicted, which can be extremely useful especially for performance-based fire safety designs. In addition, many parameters influence their performance, and this challenges the definition and quantification of their material properties. Several approaches and models of various complexities are proposed in the literature, and they are reviewed and analysed in a critical literature review.

Analytical, finite-difference and finite-element methods for modelling intumescent coatings are compared, followed by the definition and quantification of the main physical, thermal, and optical properties of intumescent coatings: swelled thickness, thermal conductivity and resistance, density, specific heat capacity, and emissivity/absorptivity.

The study highlights the scarce consideration of key influencing factors on the material properties, and the tendency to simplify the problem into effective thermo-physical properties, such as effective thermal conductivity. As a conclusion, the literature review underlines the lack of homogenisation of modelling approaches and material properties, as well as the need for a universal modelling method that can generally simulate the performance of intumescent coatings, combine the large amount of published experimental data, and reliably produce fire-safe performance-based designs.

Due to their limited applicability, high complexity and little comparability, the presented literature review does not focus on analysing and comparing different multi-component models, constituted of many model-specific input parameters. On the contrary, the presented literature review compares various approaches, models and thermo-physical properties which primarily focusses on solving the heat transfer problem through swelling intumescent systems.

The presented literature review analyses and discusses the various modelling approaches to describe and predict the behaviour of swelling intumescent coatings as fire protection for structural materials. Due to the vast variety of available commercial products and potential testing conditions, these data are rarely compared and combined to achieve an overall understanding on the response of intumescent coatings as fire protection measure. The study highlights the lack of information and homogenisation of various modelling approaches, and it underlines the research needs about several aspects related to the intumescent coating behaviour modelling, also providing some useful suggestions for future studies.

Glass material is largely used for load-bearing components in buildings. For this reason, standardized calculation methods can be used in support of safe structural design in common loading and boundary conditions. Differing from earlier literature efforts, the present study elaborates on the load-bearing capacity, failure time and fire endurance of ordinary glass elements under fire exposure and sustained mechanical loads, with evidence of major trends in terms of loading condition and cross-sectional layout. Traditional verification approaches for glass in cold conditions (i.e. stress peak check) and fire endurance of load-bearing members (i.e. deflection and deflection rate limits) are assessed based on parametric numerical simulations.

The mechanical performance of structural glass elements in fire still represents an open challenge for design and vulnerability assessment. Often, special fire-resisting glass solutions are used for limited practical applications only, and ordinary soda-lime silica glass prevails in design applications for load-bearing members. Moreover, conventional recommendations and testing protocols in use for load-bearing members composed of traditional constructional materials are not already addressed for glass members. This paper elaborates on the fire endurance and failure detection methods for structural glass beams that are subjected to standard ISO time–temperature for fire exposure and in-plane bending mechanical loads. Fire endurance assessment methods are discussed with the support of Finite Element (FE) numerical analyses.

Based on extended parametric FE analyses, multiple loading, geometrical and thermo-mechanical configurations are taken into account for the analysis of simple glass elements under in-plane bending setup and fire exposure. The comparative results show that – in most of cases – thermal effects due to fire exposure have major effects on the actual load-bearing capacity of these members. Moreover, the conventional stress peak verification approach needs specific elaborations, compared to traditional calculations carried out in cold conditions.

The presented numerical results confirm that the fire endurance analysis of ordinary structural glass elements is a rather complex issue, due to combination of multiple aspects and influencing parameters. Besides, FE simulations can provide useful support for a local and global analysis of major degradation and damage phenomena, and thus support the definition of simple and realistic verification procedures for fire exposed glass members.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

The agriculture industry has a considerable impact on Malaysia’s economy, as seen by its contribution of roughly 8.2% of gross domestic product in 2018 and its potential to absorb 11.09% of Malaysian labor in the same year. This study aims to simulate rising output in a system model of sustainable and profitable crude palm oil (CPO) supply chain management (SCM) and to formulate policy solutions to build sustainable and profitable SCM of Malaysian CPO.

This research included both primary and secondary data. This study used the dynamic system model to simulate palm oil land expansion, replanting policies and environmentally friendly growing techniques.

This study’s findings suggest that the dynamic system model of Malaysia’s CPO’s sustainable and profitable SCM is valid when its structure and performance are tested. The fifth scenario provides the best results, with the most significant net benefit value compared to the other scenarios.

The ideal policy alternative is replanting sustainable agricultural practices without burning technologies during new land clearing to achieve the best net advantages.

The purpose of this paper is to perform a comparative analysis of the delay associated in running two real-time machine learning-based applications, namely, a video quality assessment (VQA) and a phishing detection application by using the edge, fog and cloud computing paradigms.

The VQA algorithm was developed using Android Studio and run on a mobile phone for the edge paradigm. For the fog paradigm, it was hosted on a Java server and for the cloud paradigm on the IBM and Firebase clouds. The phishing detection algorithm was embedded into a browser extension for the edge paradigm. For the fog paradigm, it was hosted on a Node.js server and for the cloud paradigm on Firebase.

For the VQA algorithm, the edge paradigm had the highest response time while the cloud paradigm had the lowest, as the algorithm was computationally intensive. For the phishing detection algorithm, the edge paradigm had the lowest response time, and the cloud paradigm had the highest, as the algorithm had a low computational complexity. Since the determining factor for the response time was the latency, the edge paradigm provided the smallest delay as all processing were local.

The main limitation of this work is that the experiments were performed on a small scale due to time and budget constraints.

A detailed analysis with real applications has been provided to show how the complexity of an application can determine the best computing paradigm on which it can be deployed.

This paper aims to investigate the experimental behavior and the reliability of concrete columns repaired using fiber-reinforced polymers (FRPs) under axial compression loading. The expression of the ultimate axial resistance was assessed from the experimental data of damaged concrete cylinders repaired by externally bonded double-FRP spiral strips.

The tested columns bearing capacity mainly depends of the elasticity modulus of both damaged and undamaged concrete have been considered in addition to the applied load and the cylinder diameter as random variables in the expression of the failure criterion. The reliability indicators were assessed using first order second moment method.

The emphasized test results, statistically fitted show that the strength has been retrofitted for all repaired specimens whatever the degree of initial damage. However, the gain in axial strength is inversely proportional to the degree of damage.

The efficiency of a new FRP repair procedure using double-spiral strips was studied. This research provides a technical and economical solution for retrofitting existing concrete columns. Finally, the random character of the variables that govern the studied system shows the accuracy and safety of the proposed original design.

This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.

This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.

The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.

This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

The paper aims at studying the effect of management practices on the extensive and intensive export margins of Egyptian manufacturing firms.

The study relies on the 2020/2021 Egyptian Industrial Firm Behavior Survey (EIFBS) which comprises 2,383 manufacturing firms representing small, medium, and large sized firms located in different regions of Egypt: Urban Governorates, Lower Egypt, and Upper Egypt. It constructs an overall management z score for each firm to estimate its effect on a firm’s probability of exporting and value of exports using Ordinary Least Squares (OLS) regressions.

Results indicate that good management is associated with a higher probability of firm exporting as well as higher export revenues conditional on exporting, robust to controlling for the level of domestic sales. These effects do not differ by firm ownership or type of sector, but rather by firm size, with managerial competence raising the probability of exporting more for large-sized firms. Additionally, good management is associated with higher firm productivity, innovation and worker training propensities which gives evidence that it is both an efficiency and a quality enhancer. Moreover, monitoring and targeting practices have significant positive effects on both margins, while incentives are only significant for the extensive margin.

Firms that aim at enhancing their export prospects and revenues should devote resources to review and upgrade their management systems to boost their product quality and production efficiency. Policy-wise, the government should create a competitive market environment that is open to both domestic and foreign firms’ entry to stimulate the adoption of better management practices.

The paper is the first to explore the link between firm management practices and export outcomes for a MENA country (Egypt). It makes use of a recent survey, the 2020/2021 Egyptian Industrial Firm Behavior Survey (EIFBS). The findings shed light on the importance of different management components (monitoring, targeting and incentives) in driving a manufacturing firm’s export performance.