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In developing countries, such as Brazil, the construction sector is consistently focused on the construction of new buildings, and there is no dissemination of the preservation, restoration and maintenance of historic buildings. Idle buildings, due to the use and lack of maintenance, present pathological manifestations, such as moisture problems that compromise specially their thermal and energy performance. With this in mind, the purpose of this work is to create a digital model using terrestrial photogrammetry and suggest retrofit interventions based on computer simulation to improve the thermal and energy performance of a historical building.

The proposed methodology combined terrestrial photogrammetry using common smartphones and commercial software for historical buildings with building information modeling (historic building information modeling (HBIM)) and building energy modeling (BEM). The approach follows five steps: planning, site visit, data processing, data modeling and results. Also, as a case study, the School of Architecture and Urbanism of the Fluminense Federal University, built in 1888, was chosen to validate the approach.

A digital map of pathological manifestations in the HBIM model was developed, and interventions considering the application of expanded polystyrene in the envelope to reduce energy consumption were outlined. From the synergy between HBIM and BEM, it was concluded that the information modeled using photogrammetry was fundamental to create the energy model, and simulations were needed to optimize the possible solutions in terms of energy consumption.

Firstly, the work proposes a reasonable methodology to be applied in development countries without sophisticated technologies, but with acceptable precision for the study purpose. Secondly, the presented study shows that the use of HBIM for energy modeling proved to be useful to simulate possible solutions that optimize the thermal and energy performance.

Aluminium alloys can be used as lightweight and high-strength materials in combination with the technology of laser beam welding, an efficient joining method, in the manufacturing of automotive parts. The purposes of this paper are to conduct laser welding experiments with Al2024 in the lap joint configuration, model the laser welding process parameters of Al2024 alloys and use propounded models to optimize the process parameters.

Laser welding of Al2024 alloy has been conducted in the lap joint configuration. Then, the influences of explanatory variables (laser peak power, scanning speed and frequency) on outcome variables (weld width [WW], throat length [TL] and breaking load [BL]) have been investigated with Poisson regression analysis of the data set derived from experimentation. Thereafter, a multi-objective genetic algorithm (MOGA) has been used using MATLAB to find the optimum solutions. The effects of various input process parameters on the responses have also been analysed using response surface plots.

The promulgated statistical models, derived with Poisson regression analysis, are evinced to be well-fit ones using the analysis of deviance approach. Pareto fronts have been used to demonstrate the optimization results, and the maximized load-bearing capacity is computed to be 1,263 N, whereas the compromised WW and TL are 714 µm and 760 µm, respectively.

This work of conducting laser welding of lap joint of Al2024 alloy incorporating the Taguchi method and optimizing the input process parameters with the promulgated statistical models proffers a neoteric perspective that can be useful to the manufacturing industry.

The purpose of this study is to explore the suggestions that construction processes could be considerably improved by integrating building information modelling (BIM) with 3D laser scanning technologies. This case study integrated 3D laser point cloud scans with BIM to explore the effects of BIM adoption on ongoing construction project, whilst evaluating the utility of 3D laser scanning technology for producing structural 3D models by converting point cloud data (PCD) into BIM.

The primary data acquisition adopted the use of Trimble X7 laser scanning process, which is a set of data points in the scanned space that represent the scanned structure. The implementation of BIM with the 3D PCD to explore the precision and effectiveness of the construction processes as well as the as-built condition of a structure was precisely captured using the 3D laser scanning technology to recreate accurate and exact 3D models capable of being used to find and fix problems during construction.

The findings indicate that the integration of BIM and 3D laser scanning technology has the tendency to mitigate issues such as building rework, improved project completion times, reduced project cost, enhanced interdisciplinary communication, cooperation and collaboration amongst the project duty holders, which ultimately enhances the overall efficiency of the construction project.

The acquisition of data using 3D laser scanner is usually conducted from the ground. Therefore, certain aspects of the building could potentially disturb data acquisition; for example, the gable and sections of eaves (fascia and soffit) could be left in a blind spot. Data acquisition using 3D laser scanner technology takes time, and the processing of the vast amount of data acquired is laborious, and if not carefully analysed, could result in errors in generated models. Furthermore, because this was an ongoing construction project, material stockpiling and planned construction works obstructed and delayed the seamless capture of scanned data points.

These findings highlight the significance of integrating BIM and 3D laser scanning technology in the construction process and emphasise the value of advanced data collection methods for effectively managing construction projects and streamlined workflows.

Facility management gained profound importance due to the increasing complexity of different systems and the cost of operation and maintenance. However, due to the increasing complexity of different systems, facility managers may suffer from a lack of information. The purpose of this paper is to propose a new facility management approach that links segmented assets to the vital data required for managing facilities.

Automatic point cloud segmentation is one of the most crucial processes required for modelling building facilities. In this research, laser scanning is used for point cloud acquisition. The research utilises region growing algorithm, colour-based region-growing algorithm and Euclidean cluster algorithm.

A case study is worked out to test the accuracy of the considered point cloud segmentation algorithms utilising metrics precision, recall and F-score. The results indicate that Euclidean cluster extraction and region growing algorithm revealed high accuracy for segmentation.

The research presents a comparative approach for selecting the most appropriate segmentation approach required for accurate modelling. As such, the segmented assets can be linked easily with the data required for facility management.

In the denim industry, enzyme washing and its combination with stone washing are generally used to get the desired worn-out look. However, these conventional methods include high water, energy and time consumption. Nowadays, laser fading, which is a computer-controlled, dry, ecological finishing method, is preferred in the denim fading process. The purpose of this study is to observe the effects of chemical pretreatment applications on laser-faded denim fabric in terms of color and mechanical properties. To eliminate the enzyme washing process in denim fading and to minimize the disadvantages of laser fading, such as decreased mechanical properties and increased fabric yellowness, various chemical pretreatment applications were applied to the denim fabric before laser fading, followed by simple rinsing instead of enzyme washing.

Two different indigo-dyed, organic cotton denim fabrics with different unit weights were exposed to pretreatment processes and then laser treatment, followed by simple rinsing. Polysilicic acid, boric acid, borax and bicarbonate were used for pretreatment processes, and laser treatment was carried out under optimized laser parameters (40 dpi resolution and 300 µs pixel time). Tensile strength was tested, and color values (CIE L*, a*, b*, ΔE*, C* and h), color yield (K/S), yellowness and whiteness indexes were measured to identify the color differences.

Before laser fading, 30 g/L and 40 g/L polysilicic acid pretreatments for sulfur-indigo-dyed fabric and a mixture of 10 g/L boric acid and 10 g/L borax pretreatments for the fabric only indigo-dyed were recommended for the laser fading with sufficient mechanical properties and good color values.

With the chemical pretreatments defined in this study, it was possible to reduce yellowness and maintain the mechanical properties after laser fading, thus minimizing the disadvantages of laser treatment and also eliminating enzyme washing.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

Properly planned road illumination systems are collectively a public wealth and the commissioning of such systems may require extensive planning, simulation and testing. The purpose of this simulative work is to offer a simple approach to facilitate luminance-based road lighting calculations that can be easier to comprehend and apply to practical designing problems when compared to complex multi-objective algorithms and other convoluted simulative techniques.

Road illumination systems were photometrically simulated with a created model in a validated software platform for specified system design configurations involving high-pressure sodium (HPS) and light-emitting diode (LED) luminaires. Multiple regression analyses were conducted with the simulatively obtained data set to propound a linear model of estimating average luminance, overall uniformity of luminance and energy efficiency of lighting installations, and the simulatively obtained data set was used to explore luminaire power–road surface average luminance characteristics for common geometric design configurations involving HPS and LED luminaires, and four categories of road surfaces.

The six linear equations of the propounded linear model were found to be well-fitted with their corresponding observation sets. Moreover, it was found that the luminaire power–road surface average luminance characteristics were well-fitted with linear trendlines and the increment in road surface average luminance level per watt increment of luminaire power was marginally higher for LEDs.

This neoteric approach of estimating road surface luminance parameters and energy efficiency of lighting installations, and the compendia of luminaire power–road surface average luminance characteristics offer new insights that can prove to be very useful for practical purposes.

This study aims to provide a review for scour in complex rivers and streams with coarser bed material, steep longitudinal bed slopes and dynamic environments, in the interest of the safety and the economy of hydraulic structures. The knowledge of scour in such geographical complexities is very crucial for a comprehensive understanding of scour failures and for establishing definitive criteria to bridge this major research gap.

The existing available literature shows significant work done in case of silt, sand and small sized coarser bed material but any substantial work for bed material of gravel size or above is lacking, resulting in a wide gap. Though some researchers have attempted to explore possibilities of refining the existing models by adding pier size, shape, sediment non-uniformity and armouring effects, which otherwise have been given a miss by the various researchers, including the pioneer in the field Lacey–Inglis (1930). But still, a rational model for scour estimation in such complex conditions for global use is yet to come. This is because all the parameters governing the scour have not been studied properly till date as is evident from the globally available literature and is witnessed in the field too, in recurrent failure of hydraulic structures especially bridges.

The researchers presume that the finer materials move only as a result of erosion. However, in actual field conditions, it has been observed that the large-sized stones also roll down and cause huge erosion along the river bed and damage the hydraulic structures, especially in the steep river/stream beds along hilly slopes. This fact has been overlooked in the models available globally and has been highlighted only in the current work in an attempt to recognize this major research gap. A study carried out on a number of streams globally and in Jammu and Kashmir, India also, has shown that in steep river and stream beds with bed material consisting of gravel size or greater than gravel, large scour holes ranging from 1 m to 5 m were created by furious floods, and due to other unknown forces along the channel path and near foundations of hydraulic structures.

To the best of the authors’ knowledge, this work is purely original.

The combination of mobile devices and innovative tools offers new possibilities for the development of a community of practice for design makers. Mobile learning has become an essential method that design makers should adopt. The main content of this study is to explore the characteristics of learning behaviors and learning needs of creative design makers' group in forming a community of practice in the era of mobile learning.

This study conducted questionnaire research on the potentially associated or directly associated population of design makers. The process of the study also combined observational and interview studies to compensate for the lack of questionnaire research.

Based on the support of mobile learning technology, design makers share and co-create to achieve individual development and evolution of learning organizations, and produce creative value. Design-maker communities of practice form common communities in the framework of informal organizations to support continuous individual learning. Convergent interests or concerns in making things, real-world contexts based on makerspaces and hands-on practice based on real projects are the basis for forming design-maker communities of practice. A variety of open-source hardware, software and platforms that can support mobile learning are important for the development of design-maker communities of practice. The design-maker community of practice needs group factors, activity development, physical and technical resources, spatial support and institutional norms to enhance learning behaviors and satisfy learning needs.

The discovery and construction of these associated factors can help creative design practitioners form a lasting and virtuous organizational development. This study facilitates the formation of a social network for learning and knowledge sharing among design-maker communities of practice. It enhances the innovation ability and enthusiasm of design makers according to the population characteristics and learning needs of design makers. This study also facilitates the generation of a positive adaptive maker culture and maker spirit within design maker organizations.