S. Baduge, Sadeep Thilakarathna, J. S. Perera
et al.
This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in the facets of architectural design and visualization; material design and optimization; structural design and analysis; offsite manufacturing and automation; construction management, progress monitoring, and safety; smart operation, building management and health monitoring; and durability, life cycle analysis, and circular economy. This paper presents a unique perspective on applications of AI/DL/ML in these domains for the complete building lifecycle, from conceptual stage, design stage, construction stage, operational and maintenance stage until the end of life. Furthermore, data collection strategies using smart vision and sensors, data cleaning methods (post-processing), data storage for developing these models are discussed, and the challenges in model development and strategies to overcome these challenges are elaborated. Future trends in these domains and possible research avenues are also presented
The coherence of built architecture often depends on a latent structural design whose essential role can govern architectural outcomes even when not overtly visible. Structural logic is either integrated into the building as a whole or concealed behind finished surfaces. However, this concealed structure often defines space and regulates formal relationships. Instead of viewing structure and form as opposites or as directly corresponding, it is more accurate to understand them as mutually influential, their relationship shaped by specific design choices. Every construction act creates an order, and every form manifests through its underlying structure, highlighting an ongoing interplay between these elements. [read more]
Abstract A rapid rise in China's construction scale due to urbanization has resulted in a large amount of energy consumption and carbon emission. In order to achieve carbon emission reduction and energy security, it is important to assess the energy use of and emissions from the building construction sector. This paper presents a China Building Construction Model (CBCM) based on a process-based life cycle assessment (LCA) approach and discusses the trends in energy consumption of and carbon emission regarding China's building construction sector from 2000 to 2016. In 2016, the total energy consumption of the building construction sector was 411 million tce, accounting for approximately 9% of the country's total energy consumption. The construction activities of urban residential, public, and commercial buildings have replaced rural residential buildings as the main source of energy consumption and emission. Construction scale, building structure type, and material production efficiency are the three important driving factors. A decline in China's building construction scale, promotion of new low carbon building structures, and improvement in production efficiency may reduce future energy use and carbon emissions related to the building construction sector.
Chakkrit Luangcharoenrat, S. Intrachooto, Vachara Peansupap
et al.
Rapid growth in construction activities as a result of a growing population and urbanization in many parts of the world generates a large amount of waste from construction. To reduce and manage these wastes, a comprehensive understanding of the construction waste generation factors is needed. The purpose of this study is to identify the contributing factors of construction waste in Thailand’s construction industry. The causes of construction waste were identified through an extensive literature review. A total of 28 causes of construction waste were identified and grouped into the four categories: design and documentation, material and procurement, construction method and planning, and human resources. To determine the significant level of each factor, a structured questionnaire survey was carried out to gather information from contractors about causes of construction material waste. The results show that the categories contributing to construction waste ranks as design and documentation, human resources, construction methods and planning, and material and procurement, respectively. Meanwhile, factors from each category were also determined and ranked. Design change, inattentive working attitudes and behaviors, ineffective planning and scheduling, and material storage were among the highest impact factors on construction waste generation in each category. Identifying the significance levels of waste generation factors will help the industry’s stakeholders build suitable strategies to manage construction waste more effectively.
Slums are one of the threats to Cultural Heritage Areas (KCB) which consist of traditional houses, especially those with living museum status such as KCB Kotagede in Yogyakarta. One of the main causes of slums is the inability of the owner (heir) to provide funds to preserve the building, which requires large costs. Assistance from the government or other parties, which is often incidental, is not a sustainable solution. For this reason, efforts are needed to optimize the potential of KCB so that it can generate sustainable conservation costs. One effort is to adapt traditional houses into homestays that offer cultural experiences for tourists. The study was carried out in 4 (four) residential buildings which were selected using purposive sampling. The study method begins with identifying initial plans of traditional houses and identifying plans for developing adapted designs. Next, using the Form - Function - Meaning structural approach, an analysis was carried out to what extent the architectural meaning of a traditional house was maintained in its adaptation into a homestay. This study reveals that the adaptation of a traditional house into a homestay can be done while maintaining its architectural meaning.
Architecture, Architectural engineering. Structural engineering of buildings
In order to study the law of influence of rubber particle size on concrete frost resistance characteristics, this paper systematically evaluates the freeze–thaw characteristics of rubber concrete containing different particle sizes. Rubber concrete containing different particle sizes is subjected to 25, 50, 75, 100, and 125 freeze–thaw cycles. After the freeze–thaw cycles, the specimens are observed or measured for appearance, mass change rate, relative dynamic elastic modulus, internal damage degree, compressive strength, and tensile strength. The results show that the frost resistance of concrete mixed with rubber of different particle sizes is more excellent, and the surface of concrete specimens after different numbers of freezing and thawing cycles shows different degrees of spalling. Meanwhile, due to the presence of rubber, the compressive and tensile strengths of rubberized concrete are significantly inferior. Finally, the microscopic scanning results reveal the mechanism of rubber’s incorporation into concrete. The incorporation of rubber effectively reduces its internal pore development. To summarize, it can be seen that rubber incorporated into concrete is a worthwhile method to consider for frost resistance of engineering materials.
Abstract Fatal accidents occur frequently in building construction activities due to inherently hazardous nature. This paper aims to explore the fatal accident patterns in China’s building construction activities by using frequency analysis, correlation coefficient analysis and variance analysis. The data presented in this study come from short reports of accidents published by the Ministry of Housing and Urban-Rural Development. The results are obtained by analyzing the factors related to month, day of week, time interval of day, province, type of accident, and severity of accident. It’s mainly found that more fatal accidents occur (1) in July and August, (2) on Monday and (3) during the time intervals 10:00–11:00 and 15:00–16:00, and excessive laboring hours still exist in building construction activities. It’s also notable that relatively underdeveloped provinces have experienced higher mortality rate per hundred million yuan of gross domestic product in the building industry, especially Qinghai, Hainan and Heilongjiang. Moreover, fall is the predominant type of fatal accidents, accounting for more than 55% overall. There exists the significant correlation between the types of accidents and the severities of accidents, and each collapse and hoisting damage can cause more fatalities compared to other types. The corresponding recommendations are ultimately put forward to prevent fatal accidents of building construction activities. The patterns found in this paper can provide valuable direction for formulating accident prevention strategies.
An important element of urban landscapes is various plants, and contact with urban landscapes can promote children’s positive mood and mental health. However, few studies focus on Asian school-aged children’s mood for different urban landscapes and the factors shaping them. This study attempted to understand the variables, including plant color, familiarity, and viewing distances (setting 0 m and 2 m), using 150 landscape scenes (68 flowering plants, 50 exotic plants, and 32 foliage plants), on the effects of the landscape preferences and mood states of 119 school-aged children (55 boys and 64 girls). Then, using partial least squares path modelling analysis to display the gender difference in children’s color perception, landscape preferences, and mood states. The results show that: (1) Plant color richness, familiarity, and the proportion of non-green parts of scenes positively affected children’s mood states. (2) Flowering plants are more likely to produce positive moods than those of exotic plants and foliage plants. (3) Plant color richness and familiarity significantly and positively correlated with children’s mood states and landscape preferences. (4) Notably, gender differences exist in children’s landscape preferences and mood states. This study underscores the importance of plant color collocation in child-friendly landscapes and considers the gender differences in urban landscape policy decisions. Besides, adding flowering plants and native plants in urban landscapes may potentially enhance children’s mood state and urban green space utilization rate.
City identity and branding have traditionally been shaped by iconic architecture, including monumental buildings and landmarks. However, recent studies have begun to explore city branding through corporate social responsibility (CSR) initiatives, particularly from a landscape perspective. There is limited research, particularly in the Global South, examining the role of CSR in the enhancement of open green spaces and its contribution to shaping urban landscape identity. This study investigates CSR-driven beautification projects in Kisumu City, Kenya, where corporate-funded initiatives have sought to improve urban landscapes through the landscaping of road islands and roundabouts, alongside broader urban renewal efforts. The article aims to document the spatial attributes of these CSR projects; assess their current status in terms of cleanliness, safety, and maintenance, as well as identify the challenges faced in implementing CSR-driven beautification efforts. The aim of these projects is not only to promote the city’s unique urban identity, but also to enhance the corporate image through landscape branding. A qualitative research methodology is employed, involving field observations, GIS mapping, and photography, complemented by purposive interviews. The findings reveal not only diverse landscape designs, including water features and sculptures, but also highlight significant issues with upkeep, safety concerns from street urchins, and the overall deterioration of some sites. These results are valuable for policymakers in city governments and corporations involved in CSR projects, offering insights into how such initiatives can better contribute to urban landscape identity, city branding, and the sustainability of urban beautification efforts.
Carlos Alberto Torres Montes de Oca, José Eduardo Rosas Valencia, Oswaldo Aldair Pérez Jarquín
Many historic buildings have symmetry in their geometric configuration. The objective of this research is to denote the application of numerical-vector succession in the structural analysis of historical masonry buildings, with arches and symmetrical systems, including mathematical processes in ancient graphic analysis, emphasizing the importance of loads in the structural stability. We based the analysis on three fundamental stages: recognition of the construction system of the heritage object, geometric discretization of the system and vector analysis under different physical considerations. Hence, the thrust lines are affected by the loads, boundary conditions and history of structural behaviour. Numerical and computational tools offer faster and more accurate graphic analysis processes.
Many historic buildings have symmetry in their geometric configuration. The objective of this research is to denote the application of numerical-vector succession in the structural analysis of historical masonry buildings, with arches and symmetrical systems, including mathematical processes in ancient graphic analysis, emphasizing the importance of loads in the structural stability. We based the analysis on three fundamental stages: recognition of the construction system of the heritage object, geometric discretization of the system and vector analysis under different physical considerations. Hence, the thrust lines are affected by the loads, boundary conditions and history of structural behaviour. Numerical and computational tools offer faster and more accurate graphic analysis processes.
The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. However, no existing studies in the building energy systems literature have quantified the economic benefits of data collection strategies to determine whether they are worth their cost. This work demonstrates that Value of Information analysis (VoI), a Bayesian Decision Analysis framework, provides a suitable methodology for quantifying the benefits of data collection. Three example decision problems in building energy systems are studied: air-source heat pump maintenance scheduling, ventilation scheduling for indoor air quality, and ground-source heat pump system design. Smart meters, occupancy monitoring systems, and ground thermal tests are shown to be economically beneficial for supporting these decisions respectively. It is proposed that further study of VoI in building energy systems would allow expenditure on data collection to be economised and prioritised, avoiding wastage.
Abstract Buildings contribute to 40% of total global energy consumption, which is responsible to 38% of greenhouse gas emissions. It is critical to enhance the energy efficiency of buildings to mitigate global warming. In the last decade, advances in thermal energy storage (TES) techniques using phase change material (PCM) have gained much attention among researchers, mainly to reduce energy consumption and to promote the use of renewable energy sources such as solar energy. PCM technology is one of the most promising technologies available for the development of high performance and energy-efficient buildings and, therefore, considered as one of the most effective and on-going fields of research. The main limitation of PCM is its leakage problem which limits its potential use in building construction and other applications such as TES and textiles, which can be overcome by employing nano-/micro-encapsulation technologies. This paper comprehensively overviews the nano-/micro-encapsulation technologies, which are mainly classified into three categories including physical, physiochemical and chemical methods, and the properties of microcapsules prepared. Among all encapsulation technologies available, the chemical method is commonly used since it offers the best technological approach in terms of encapsulation efficiency and better structural integrity of core material. There is a need to develop a method for the synthesis of nano-encapsulated PCMs to achieve enhanced structural stability and better fracture resistance and, thus, longer service life. The accumulated database of properties/performance of PCMs and synthesised nano-/micro-capsules from various techniques presented in the paper should serve as the most useful information for the production of nano-/micro-capsules with desirable characteristics for building construction application and further innovation of PCM technology.
With various emerging technologies and integration possibilities, smart facility management has gained wide interest in recent years. Several technologies were introduced to support facilities management and improve decision-making, such as Building Information Modeling (BIM), Internet of Things (IoT), Digital Twin (DT), artificial intelligence (AI), and blockchain. Yet, facility managers still face challenges related to data handling and the actual implementation of these technologies. Thus, this paper explores the trends and integration possibilities of smart facilities management technologies to provide a deeper understanding of the current research state and the areas for future exploration. The Scopus database is utilized to collect literature data, and a bibliometric analysis is conducted on 7236 publications of different types, including conference publications, articles, reviews, and book chapters, using VOSviewer software. The results revealed a noticeable growth in the annual number of publications related to this field after 2018. BIM, IoT, and DT were seen to share the greatest research attention, with BIM being the dominant technology. With recent wide attention, blockchain technology is noticed to be introducing many integration possibilities. In addition, the prominent contributing authors, countries, and sources to this research area are also identified.
Regarding climate change, the need to reduce greenhouse gas emissions is well-known. As building heating contributes to a high share of total energy consumption, which relies mainly on fossil energy sources, improving heating efficiency is promising to consider. Lowering supply temperatures of the heating systems in buildings offers a huge potential for efficiency improvements since different heat supply technologies, such as heat pumps or district heating, benefit from low supply temperatures. However, most estimations of possible temperature reductions in existing buildings are based on available measurement data on room level or detailed building information about the building's physics to develop simulation models. To reveal the potential of temperature reduction for several buildings and strive for a wide applicability, the presented method focuses on estimations for temperature reduction in existing buildings with limited input data. By evaluating historic heat demand data on the building level, outdoor temperatures and information about installed heaters, the minimal actual necessary supply temperature is calculated for each heater in the building using the LMTD approach. Based on the calculated required supply temperatures for each room at different outdoor temperatures, the overall necessary supply temperatures to be provided to the building are chosen. Thus, the minimal heatcurve possible for an existing building is deduced. The method described is applied to multiple existing office buildings at the campus of Forschungszentrum Juelich, Germany, demonstrating the fast application for several buildings with limited expenditure. Furthermore, a developed adapted heatcurve is implemented in one real building and evaluated in relation to the previously applied heatcurve of the heating system.
In this chapter, we argue that it is highly beneficial for the contemporary construction grammarian to have a thorough understanding of the strong relationship between the research fields of construction grammar and artificial intelligence. We start by unravelling the historical links between the two fields, showing that their relationship is rooted in a common attitude towards human communication and language. We then discuss the first direction of influence, focussing in particular on how insights and techniques from the field of artificial intelligence play an important role in operationalising, validating and scaling constructionist approaches to language. We then proceed to the second direction of influence, highlighting the relevance of construction grammar insights and analyses to the artificial intelligence endeavour of building truly intelligent agents. We support our case with a variety of illustrative examples and conclude that the further elaboration of this relationship will play a key role in shaping the future of the field of construction grammar.
Let $h$ be a connective homology theory. We construct a functorial relative plus construction as a Bousfield localization functor in the category of maps of spaces. It allows us to associate to a pair $(X, H)$ consisting of a connected space $X$ and an $h$-perfect normal subgroup $H$ of the fundamental group $π_1(X)$ an $h$-acyclic map $X \rightarrow X^{+h}_H$ inducing the quotient by $H$ on the fundamental group. When $h$ is an ordinary homology theory with coefficients in a commutative ring with unit $R$, this provides a functorial and well-defined counterpart to a construction by cell attachment introduced by Broto, Levi, and Oliver in the spirit of Quillen's plus construction. We also clarify the necessity to use a strongly $R$-perfect group $H$ in characteristic zero.
Rapid renovation of Europe's inefficient buildings is required to reduce climate change. However, analyzing and evaluating buildings at scale is challenging because every building is unique. In current practice, the energy performance of buildings is assessed during on-site visits, which are slow, costly, and local. This paper presents a building point cloud dataset that promotes a data-driven, large-scale understanding of the 3D representation of buildings and their energy characteristics. We generate building point clouds by intersecting building footprints with geo-referenced LiDAR data and link them with attributes from UK's energy performance database via the Unique Property Reference Number (UPRN). To achieve a representative sample, we select one million buildings from a range of rural and urban regions across England, of which half a million are linked to energy characteristics. Building point clouds in new regions can be generated with the open-source code published alongside the paper. The dataset enables novel research in building energy modeling and can be easily expanded to other research fields by adding building features via the UPRN or geo-location.