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\"The worlds extant building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. In 2020, buildings and construction accounted for 36% of global final energy consumption and 37% of energy related CO2 emissions. The EU estimates that up to 75% of the EUs existing building stock has poor energy performance, 8595% of which will still be in use in 2050. To meet the goals of the Paris Agreement on Climate Change will require a transformation of construction processes and deep renovation of the extant building stock. It is widely recognized that ICTs can play an important role in construction, renovation and maintenance as well as supporting the financing of deep renovation. Technologies such as sensors, big data analytics and machine learning, BIM, digital twinning, simulation, robots, cobots and UAVs, and additive manufacturing are transforming the deep renovation process, improving sustainability performance, and developing new services and markets. This open access book defines a deep renovation digital ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research, and offering perspectives from business, technology and industry domains.\" -- Provided by publisher.

Liquefaction prediction is an important issue in the seismic design of engineering structures, and research on this topic has been continuing in current literature using different methods, including experimental, numerical, or soft computing. In this paper, three robust machine learning (ML) algorithms are applied to predict soil liquefaction using a set of 411 shear wave velocity case records. The Genetic Algorithm (GA) based feature selection (FS) and parameter optimization of Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGBoost) algorithms are utilized to improve the accuracy of the liquefaction prediction models. Simple Random Sampling (SRS) and Stratified Random Sampling (StrRS) are used for data sampling, and also SMOTE algorithm are applied to prepare the balanced training sets. The results of robust ML algorithms are assessed based on well-known five performance matrices, namely Accuracy (Acc), Kappa, Precision, Recall, and F-Measure. Evaluation of the results is made separately for each ML algorithm considering sampling data generated from SRS, StrRS, and SMOTE. As a result, the XGBoost model is more accurate (Acc = 96%) than RF (Acc = 93%) and SVM (Acc = 91%) in the case of the SMOTE algorithm. This study reveals the superiority of the XGBoost algorithm in the liquefaction prediction and shows how the accuracy measures tend to improve when the predictive models are trained using balanced samples with StrRS and SMOTE sampling strategies.

This article explores the possible ramifications of incorporating ideas from AEC Industry 6.0 into the design and construction of intelligent, environmentally friendly, and long-lasting structures. This statement highlights the need to shift away from the current methods seen in the AEC Industry 5.0 to effectively respond to the increasing requirement for creative and environmentally sustainable infrastructures. Modern building techniques have been made more efficient and long-lasting because of AEC Industry 6.0’s cutting-edge equipment, cutting-edge digitalization, and ecologically concerned methods. The academic community has thoroughly dissected the many benefits of AEC Industry 5.0. Examples are increased stakeholder involvement, automation, robotics for optimization, decision structures based on data, and careful resource management. However, the difficulties of implementing AEC Industry 6.0 principles are laid bare in this research. It calls for skilled experts who are current on the latest technologies, coordinate the technical expertise of many stakeholders, orchestrate interoperable standards, and strengthen cybersecurity procedures. This study evaluates how well the principles of Industry 6.0 can create smart, long-lasting, and ecologically sound structures. The goal is to specify how these ideas may revolutionize the building industry. In addition, this research provides an in-depth analysis of how the AEC industry might best adopt AEC Industry 6.0, underscoring the sector-wide significance of this paradigm change. This study thoroughly analyzes AEC Industry 6.0 about big data analytics, the IoT, and collaborative robotics. To better understand the potential and potential pitfalls of incorporating AEC Industry 6.0 principles into the construction of buildings, this study examines the interaction between organizational dynamics, human actors, and robotic systems.

Despite significant research advances on the seismic response analysis, there is still an urgent need for validation of numerical simulation methods for prediction of earthquake response and damage. In this respect, seismic monitoring networks and proper modelling can further support validation studies, allowing more realistic simulations of what earthquakes can produce. This paper discusses the seismic response of the “Pietro Capuzi” school in Visso, a village located in the Marche region (Italy) that was severely damaged by the 2016–2017 Central Italy earthquake sequence. The school was a two-story masonry structure founded on simple enlargements of its load-bearing walls, partially embedded in the alluvial loose soils of the Nera river. The structure was monitored as a strategic building by the Italian Seismic Observatory of Structures (OSS), which provided acceleration records under both ambient noise and the three mainshocks of the seismic sequence. The evolution of the damage pattern following each one of the three mainshocks was provided by on-site survey integrated by OSS data. Data on the dynamic soil properties was available from the seismic microzonation study of the Visso village and proved useful in the development of a reliable geotechnical model of the subsoil. The equivalent frame (EF) approach was adopted to simulate the nonlinear response of the school building through both fixed-base and compliant-base models, to assess the likely influence of soil–structure interaction on the building performance. The ambient noise records allowed for an accurate calibration of the soil–structure model. The seismic response of the masonry building to the whole sequence of the three mainshocks was then simulated by nonlinear time history analyses by using the horizontal accelerations recorded at the underground floor as input motions. Numerical results are validated against the evidence on structural response in terms of both incremental damage and global shear force–displacement relationships. The comparisons are satisfactory, corroborating the reliability of the compliant-base approach as applied to the EF model and its computational efficiency to simulate the soil–foundation–structure interaction in the case of masonry buildings.

Indoor air quality has been the object of interest for scientists and specialists from the fields of science such as chemistry, medicine and ventilation system design. This results from a considerable number of potential factors, which may influence the quality of the broadly understood indoor air in a negative way. Poor quality of indoor air in various types of public utility buildings may significantly affect an increase in the incidence of various types of civilisation diseases. This paper presents information about a broad spectrum of chemical compounds that were identified and determined in the indoor environment of various types of public utility rooms such as churches, museums, libraries, temples and hospitals. An analysis of literature data allowed for identification of the most important transport paths of chemical compounds that significantly influence the quality of the indoor environment and thus the comfort of living and the health of persons staying in it.

Slow-moving landslides yearly induce huge economic losses worldwide in terms of damage to facilities and interruption of human activities. Within the landslide risk management framework, the consequence analysis is a key step entailing procedures mainly based on identifying and quantifying the exposed elements, defining an intensity criterion and assessing the expected losses. This paper presents a two-scale (medium and large) procedure for vulnerability assessment of buildings located in areas affected by slow-moving landslides. Their intensity derives from Differential Interferometric Synthetic Aperture Radar (DInSAR) satellite data analysis, which in the last decade proved to be capable of providing cost-effective long-term displacement archives. The analyses carried out on two study areas of southern Italy (one per each of the addressed scales) lead to the generation, as an absolute novelty, of both empirical fragility and vulnerability curves for buildings in slow-moving landslide-affected areas. These curves, once further validated, can be valuably used as tools for consequence forecasting purposes and, more in general, for planning the most suitable slow-moving landslide risk mitigation strategies.

In order to realize low-cost, fast, and accurate fracture identification in lining quality inspection and underground engineering stability evaluation, we propose an intelligent fracture identification method, which can achieve depth extraction of fracture information from rock images. Firstly, the fracture identification model combined with the residual network was constructed to obtain more fracture features. Secondly, dilated convolution layers were added to improve the resolution of the fracture feature points. Meanwhile, the perceptual field is expanded to achieve the effective capture of characteristics of tiny fractures. Lastly, the Rectified Linear Unit (ReLU) function is introduced to realize the nonlinear transformation of the model. And the predicted results are optimized by Dice loss function to accelerate the convergence of the model. The model is applied to identify concrete fractures and rock fractures. We also conduct reliability assessment and then compare the performance of our identification model to LinkNet, U-net, CNN + ASPP models. The maximum accuracy (98.93%), recall (83.78%) values of the method far exceed that CNN + ASPP model using the same dataset. High image similarity and smaller fracture features degrade the performance of the image identification method, while the method can significantly overcome these challenges and thus greatly improve the accuracy of fracture identification. And the identification method is more accurate and robust than the comprised method. Besides the proposed method with good generalization and can be used for the rapid identification of rock mass fractures in highways, constructions, slopes, water conservancies and other projects.

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection. However, as a step towards theoretically aware, impactful, and reproducible research, a more rigorous and transparent method of data processing is required. To this end, we set out to create a processing pipeline for archaeology-specific point cloud processing and derivation of products that are optimized for general-purpose data. The proposed pipeline improves on ground and building point cloud classification. The main area of innovation in the proposed pipeline is raster grid interpolation. We have improved the state-of-the-art by introducing a hybrid interpolation technique that combines inverse distance weighting with a triangulated irregular network with linear interpolation. State-of-the-art solutions for enhanced visualizations are included and essential metadata and paradata are also generated. In addition, we have introduced a QGIS plug-in that implements the pipeline as a one-step process. It reduces the manual workload by 75 to 90 percent and requires no special skills other than a general familiarity with the QGIS environment. It is intended that the pipeline and tool will contribute to the white-boxing of archaeology-specific airborne LiDAR data processing. In discussion, the role of data processing in the knowledge production process is explored.

The seismic vulnerability assessment of old masonry buildings is essential not only to buildings with recognised historical and heritage value but also to ordinary residential masonry buildings. Thus, and resorting a wide set of damage data collected after the 1998 Azores earthquake, this paper addresses the topic of the seismic vulnerability assessment of traditional masonry buildings through the calibration and application of a simplified vulnerability assessment method to the old city centre of Horta (Faial Island), in Portugal. In this case study, 192 buildings were assessed using this methodology, and vulnerability and loss scenarios were analysed using an integrated Geographical Information System tool. As is discussed in this paper, such procedure could allow city councils or regional authorities to plan interventions based on a global view of the site under analysis, leading to more accurate and comprehensive risk mitigation strategies, which can than support the requirements of safety and emergency planning.

A multiple level-of-detail (LOD) simulation framework is proposed in this study, to take full consideration of the diversity of structural types, available data, and simulation scenarios in an actual application of seismic-damage simulation to urban buildings. Firstly, key features of the frequently used seismic simulation methods for buildings are discussed, and logical relationships of these simulation methods, as well as the available multi-source data, are established in different LODs. Secondly, implementation of the proposed multi-LOD simulation framework is presented, and a unified city data structure is proposed to enable effective management and storage of data with different LODs. Finally, the Beijing central business district, which has various types of buildings, is investigated in detail to demonstrate the proposed multi-LOD framework. The accuracy, efficiency, and corresponding requirements of different LOD simulations are compared and discussed. The outcomes of this work are expected to provide a useful reference for the application of seismic-damage simulations in complex urban areas.