Search Results (5,201)

To address the issues of blurred edges and contours, insufficient extraction of low-frequency information, and unclear texture details in ancient murals, which lead to decreased ornamental value and limited research significance of the murals, this paper proposes a novel ancient mural super-resolution reconstruction method, based on an attention mechanism and a multi-level residual network, termed AM-ESRGAN. This network builds a module for Multi-Scale Dense Feature Fusion (MDFF) to adaptively fuse features at different levels for more complete structural information regarding the image. The deep feature extraction module is improved with a new Sim-RRDB module, which expands capacity without increasing complexity. Additionally, a Simple Parameter-Free Attention Module for Convolutional Neural Networks (SimAM) is introduced to address the issue of insufficient feature extraction in the nonlinear mapping process of image super-resolution reconstruction. A new feature refinement module (DEABlock) is added to extract image feature information without changing the resolution, thereby avoiding excessive loss of image information and ensuring richer generated details. The experimental results indicate that the proposed method improves PSNR/dB by 3.4738 dB, SSIM by 0.2060, MSE by 123.8436, and NIQE by 0.1651 at a $\times 4$ scale factor. At a $\times 2$ scale factor, PSNR/dB improves by 4.0280 dB, SSIM increases by 3.38%, MSE decreases by 62.2746, and NIQE reduces by 0.1242. Compared to mainstream models, the objective evaluation metrics of the reconstructed images achieve the best results, and the reconstructed ancient mural images exhibit more detailed textures and clearer edges. Full article

The construction industry is constantly evolving through government policies, technologies, and innovative processes. BIM and modular construction are innovative concepts aimed at achieving sustainable smart cities by enhancing cost performance, efficiency, and sustainability. Despite growing global interest in their integration, there is a notable knowledge gap in sub-Saharan Africa. As a result, this research aims to explore the barriers to integrating BIM into modular construction in sub-Saharan Africa. The study adopted a non-experimental design, using a four-stage methodological framework. Initially, a literature review was carried out to conceptualize the study. Stage two involves a pilot survey to create an adequate data collection instrument. In the third stage, 81 registered companies were purposely selected, and data was collected through an online survey. Finally, the fourth stage uses descriptive and inferential techniques to make logical and informed conclusions. The top-ranked barriers are high initial costs, insufficient cross-field expertise, stakeholder collaboration problems, limited software interoperability, and skills shortages. Recommendations include early stakeholder collaboration, BIM execution plan development by modular companies, improved staff training, and increasing financial support from the government. Future research should explore country-specific barriers and case studies to aid the integration of the two innovative solutions in the region. Full article

Energy modelling plays a crucial role in assisting governmental and policymaking bodies to strategise long-term investments within the context of energy transition. Among the well-established open-source optimisation models, OSeMOSYS—the Open-Source Energy Modelling System—stands out. This paper introduces clicSAND, a novel user interface designed for OSeMOSYS, aimed at reducing the learning curve and supporting novice energy modelers in efficiently conducting long-term investment analyses. clicSAND, freely available and open-source, features a user-friendly Excel interface for data input, integrated solvers, and a visualisation dashboard for result interpretation. The outcomes, projected up to 2070, hold the potential to inform policy decisions and mobilise financial resources for sustainable development endeavors, such as ensuring affordable and secure energy supply and mitigating climate change impacts. This advancement not only democratises access to energy modelling tools but also empowers policymakers and stakeholders to conduct thorough long-term investment analyses with ease. This paper elaborates on clicSAND’s key advantages, architecture, and functionalities. Additionally, it discusses the evolutionary journey from clicSAND 1.0 to 3.0, emphasising a commitment to continuous improvement and user-centric adaptation, thereby enhancing its utility and relevance. The inclusion of a South African case study, conducted during the EMP-A (Energy Modelling Platform for Africa) 2021 international capacity-building event, showcases clicSAND’s efficacy in facilitating knowledge transfer and skill development among inexperienced users, while providing a tangible example of its application in addressing specific regional energy challenges and policy contexts. Finally, current applications and future extensions of the software are also presented. Full article

The conversion from building information modeling (BIM) to building energy modeling (BEM) based on the industry foundation classes (IFC) data standard is a crucial step for efficient building energy design and energy performance analysis. The scope encompasses analyzing limitations in existing BIM-to-BEM workflows and proposing an optimized strategy that addresses data loss and modeling inconsistencies. The research question revolves around enhancing conversion efficiency and precision, with the hypothesis validated through literature review, development of a conversion tool, and case study verification. The data collection and evaluation methods involve streamlining the conversion process by incorporating BIM model optimization, automatic repair of damaged geometric information, and automatic thermal zone division. The main findings reveal that the optimized strategy and tool significantly reduce information duplication, improve the precision of energy simulations, and validate the hypothesis, thereby contributing to more efficient and accurate building energy design and analysis. Full article

This study introduces a new software, cLASpy_T, that helps design models for the automatic 3D point cloud classification of coastal environments. This software is based on machine learning algorithms from the scikit-learn library and can classify point clouds derived from LiDAR or photogrammetry. Input data can be imported via CSV or LAS files, providing a 3D point cloud, enhanced with geometric features or spectral information, such as colors from orthophotos or hyperspectral data. cLASpy_T lets the user run three supervised machine learning algorithms from the scikit-learn API to build automatic classification models: RandomForestClassifier, GradientBoostingClassifier and MLPClassifier. This work presents the general method for classification model design using cLASpy_T and the software’s complete workflow with an example of photogrammetry point cloud classification. Four photogrammetric models of a coastal dike were acquired on four different dates, in 2021. The aim is to classify each point according to whether it belongs to the ‘sand’ class of the beach, the ‘rock’ class of the riprap, or the ‘block’ class of the concrete blocks. This case study highlights the importance of adjusting algorithm parameters, selecting features, and the large number of tests necessary to design a classification model that can be generalized and used in production. Full article

The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned was tantamount to an “environmental nuclear bomb”. Shortly thereafter, an unpredicted and unprecedented pluvial winter resulted in an increase in inflow, temporarily halting the lake’s decline and prompting Utah’s governor to mock the dire prediction as “a joke”, an outcome that speaks to the tension between agenda-setting and trust-building that researchers face when sharing worst-case warnings, particularly those based on short-term variability. Here, we describe a robust relationship between the lake and groundwater in the surrounding region and demonstrate how coupled models can thus be used to improve lake elevation predictions, suggesting that while the situation may not be as dire as some have warned, the lake remains at long-term risk as a result of climate warming. We further suggest that efforts to communicate the risk of future desiccation should be informed by stochastic variability and guided by long-term fluctuations in the total water storage of the endorheic lake’s watershed. Full article

This study explores the transformative potential of integrating Building Information Modeling (BIM) and Generative Design methodologies in heritage conservation and building management. By utilizing BIM, detailed architectural, structural, and MEP models were created, facilitating precise design and effective stakeholder collaboration. Generative Design enabled the exploration of multiple design solutions, optimizing spatial layouts and structural integrity. The project also integrated automated management systems and IoT sensors to enhance real-time monitoring, energy efficiency, and user comfort through the development of a digital twin. Despite encountering challenges such as technical complexities and budget constraints, the project successfully preserved the cinema’s historical essence while incorporating modern functionalities. The findings highlight the contributions of BIM and Generative Design to the AEC industry, emphasizing their role in improving design accuracy, operational efficiency, and sustainability. This research provides valuable insights for future projects in heritage conservation, offering a blueprint for balancing historical preservation with contemporary needs. The revitalization of the “Ex Cinema Santa Barbara” in Paternò exemplifies these advancements, demonstrating how these technologies can restore and modernize culturally significant historical buildings effectively. Full article

In this paper, we propose a comprehensive and optimised workflow for the documentation and the future maintenance and management of a historical building, integrating the state of the art of different techniques, in the challenging context of industrial archaeology. This approach has been applied to the hydraulic work of the “Sostegno del Battiferro” in Bologna, Italy, an example of built industrial heritage whose construction began in 1439 and remains in active use nowadays to control the Navile canal water flow rate. The initial step was the definition of a 3D topographic frame, including geodetic measurements, which served as a reference for the complete 3D survey integrating Terrestrial Laser Scanning (TLS), Structured Light Projection scanning, and the photogrammetric processing of Unmanned Aircraft System (UAS) imagery through a Structure from Motion (SfM) approach. The resulting 3D point cloud has supported as-built parametric modelling (Scan-to-BIM) with the consequent extraction of plans and sections. Finally, the Heritage/Historic Building Information Modelling (HBIM) model generated was rendered and tested for a VR-based immersive experience. Building Information Modelling (BIM) and virtual reality (VR) applications were tested as a support for the management of the building, the maintenance of the hydraulic system, and the training of qualified technicians. In addition, considering the historical value of the surveyed building, the methodology was also applied for dissemination purposes. Full article

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users’ requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi’s production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry. Full article

Preserving historical architectural structures is crucial for safeguarding cultural heritage. This study explores the application of Heritage Building Information Modelling (HBIM) to enhance the documentation process of the Ghiqa Historical Market in Saudi Arabia, a monument known for its intricate architecture and cultural significance. Traditional documentation methods often fail to capture detailed features accurately and rely on labour-intensive manual processes. HBIM uses advanced digital technologies to improve precision, efficiency, and preservation efforts. In this study, point cloud data from 3D laser scanning is used to create a detailed digital model of the market, covering structural systems, material attributes, architectural features, and historical context. The research also integrates historical archives and photographs to enrich the model with additional contextual information. This comprehensive approach provides a holistic understanding of the Ghiqa Historical Market, aiding accurate preservation and restoration decisions. HBIM offers several advantages in architectural documentation. The digital model enhances visualization, allowing stakeholders to explore the site from multiple perspectives. It also serves as a tool for analysing structural integrity, identifying potential risks, and planning restoration interventions. Moreover, digital documentation ensures effective knowledge transfer across generations, preserving valuable architectural heritage for future reference and research. Additionally, it promotes interdisciplinary collaboration among architects, historians, conservators, and other stakeholders involved in preservation. Including the Ghiqa Historical Market in the UNESCO World Heritage List would highlight its global significance, attracting international attention and resources for its preservation. This designation would underscore the market’s cultural and historical importance, fostering a sense of pride and responsibility in its maintenance. The implementation of Heritage BIM demonstrates its potential to revolutionize heritage conservation by combining diverse data sources into a single, detailed, and accessible digital resource. Full article

Non-destructive testing (NDT) techniques are crucial in making informed decisions about reconstructing or repairing building structures. The SonReb method, a combination of the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) tests, is widely used for this purpose. To evaluate the compressive strength, $CS$, of the concrete under investigation, the ultrasonic pulse velocity $V_p$ and the rebound index R must be mapped to the compressive strength $CS$ using a suitable conversion model, the identification of which requires supplementing the NDT measurements with destructive-type measurements (DT) on a relatively large number of concrete cores. An approach notably indicated in all cases where the minimization of the number of cores is essential is to employ a pre-existing conversion model, i.e., a model derived from previous studies conducted in the literature, which must be appropriately calibrated. In this paper, we investigate the performance of Gaussian process regression (GPR) in calibrating the pre-existing SonReb conversion models, exploiting their ability to handle nonlinearity and uncertainties. The numerical results obtained using experimental data collected from the literature show that GPR calibration is very effective, outperforming, in most cases, the standard multiplicative and additive techniques used to calibrate the SonReb models. Full article

Singapore has established six Integrated Construction and Prefabrication Hubs with the goal of meeting ambitious productivity targets and building a resilient precast supply chain by 2024. These factories are equipped with high levels of mechanization and automation. However, they are currently operating far below their designed capacity due to a storage bottleneck. In land-scarce Singapore, finding large spaces for precast storage is a challenge. One possible solution is to implement a just-in-time approach. To achieve this, a systematic approach is required to plan, monitor, and control the entire supply chain effectively, utilizing various strategies, methods, and tools. This paper aims to conduct a comprehensive literature review in related areas, believing that knowledge transfer is a faster way to develop solutions to new problems. The main idea of the proposed solution is to implement an integrated supply chain system model with a central decision-maker. It is recommended that the factories take a more active role in decision-making. Establishing this integrated system relies on trust and information sharing, which can be facilitated by cutting-edge digital technologies. The results of this paper will provide valuable insights for future research aimed at completely solving this issue. Full article

In this study, we propose the CVMFCC-DR (Complex-Valued Mel-Frequency Cepstral Coefficients Dimensionality Reduction) algorithm as an efficient method for reducing the dimensionality of speech signals. By utilizing the complex-valued MFCC technique, which considers both real and imaginary components, our algorithm enables dimensionality reduction without information loss while decreasing computational costs. The efficacy of the proposed algorithm is validated through experiments which demonstrate its effectiveness in building a speech recognition model using a complex-valued neural network. Additionally, a complex-valued softmax interpretation method for complex numbers is introduced. The experimental results indicate that the approach yields enhanced performance compared to traditional MFCC-based techniques, thereby highlighting its potential in the field of speech recognition. Full article

The integration of urban agriculture into cityscapes necessitates a comprehensive understanding of multiple engineering and environmental factors, including urban fabric, building configurations, and dynamic energy and material flows. In contrast to rural settings, urban areas introduce complexities such as hygrothermal fluctuations, variable sunlight exposure and shadow patterns, diverse plant dimensions and shapes, and material interception. To address these challenges, this study presents an open-source Digital Twin model based on the use of a geographical information system (GIS) for near-real-time solar radiation mapping. This methodology aims to optimize crop productivity, enhance resilience, and promote environmental sustainability within urban areas and enables the near-time mapping of the salient features of different portions of the city using available open data. The work is structured into two main parts: (i) definition of the GIS-based Digital Twin model for mapping microclimatic variables (in particular solar radiation) to support sustainable urban agriculture design and (ii) application of the model to the city of Milan to verify its replicability and effectiveness. The key findings are connected to the possibility to integrate open data (solar radiation) with measurements in situ (illuminance and data referred to the specific crops, with related conversion coefficient) to develop a set of maps helpful for urban farmers but also for designers dealing with the synergy between buildings and urban farms. Initially tested on a neighborhood of Milan (Italy), the model will be applied in the Singapore context to verify analogies and differences. This correlation facilitates a more practical and straightforward examination of the relationships between solar irradiation and illuminance values of natural sunlight (involving both incident and diffuse light). The consistency of measurements allows for the precise documentation of these fluctuations, thereby enhancing the understanding of the influence of solar radiation on perceived luminance levels, particularly in urban environments characterized by diverse contextual factors such as vegetation, nearby structures, and geographical positioning. Full article

This review article examines global initiatives in the adoption of building information modeling (BIM) for road infrastructure. It begins with an overview of the distinctions between BIM applications for buildings and infrastructure projects. This study evaluates noteworthy BIM publications (NBPs) from various countries and organizations to understand BIM’s transformative impact on roadway infrastructure projects. It analyzes the evolution of these publications, compares academic output with NBP, identifies the stages of BIM maturity, and evaluates adherence to ISO 19650 standards. Through this analysis, the article presents current global and regional scenarios, providing a comprehensive overview of the current state and future prospects of BIM implementation in the road infrastructure sector. Full article