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Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

Successful project delivery for sustainable building construction (SBC) has been linked to certain features. Previous studies have emphasised the need to improve SBC practice in South Africa. The purpose of this study is to explore the SBC features for project delivery in South Africa.

A structured questionnaire elicited the primary data from 281 built environment professionals, mainly in South Africa’s Gauteng province. Descriptive and inferential statistics were used for the data analysis. This study used the principal component analysis technique to ascertain the principal SBC features.

Three components of SBC features, namely, sustainable resource use and compliance, sustainable waste minimisation and recycling and sustainable designs and materials, were developed from the principal component analysis. The factor loadings of the constituent variables ranged from 0.570 to 0.836. The reliability of each component was evaluated, and the results were 0.966, 0.931 and 0.913.

The revelations from this study will aid the decision-making of the relevant stakeholders towards establishing improvement initiatives and mitigating the reluctance to shift from conventional building methods and poor knowledge sharing of SBC benefits.

This is one of the most recent South African studies that sheds light on the components of a successful SBC deployment. The findings of this study added to knowledge by confirming three fundamental features of SBC. This study recommends adequately considering the principal features for successful SBC project delivery in South Africa.

Despite worldwide climate change and the problems caused by using fossil fuels, energy consumption in the world keeps rising every year. The areas with extremely cold or scorching climates are large, and significant amounts of energy are getting used in these areas for heating, cooling, and ventilation. The general purpose of this study is to investigate the possible relationship between the climatic characteristics of the Esfahak, a village located in the hot desert region of Iran, and the physical characteristics of its built environment.

The method of this research is qualitative and somewhat descriptive-analytical. In this regard, the architectural features of Esfahak village are compared with the principles mentioned in the Mahoney tables to determine the degree of compliance of the architecture of this village with the climatic condition.

The results show that design principles have been used in all indicators discussed in the Mahoney tables. By applying these principles, not only did the acute weather conditions not prevent the initial settlement in the village location, they have not caused inhabitants to leave the site over time as well.

The impacts of bioclimatic design strategies on thermal comfort in hot desert regions are seldom studied. This research provides evidence-based and informed design recommendations that can help building designers and city authorities integrate bioclimatic design strategies at the earliest conceptual design phases in hot desert climates.

The purpose of this paper is to investigate the effect of the replacement of natural coarse aggregate (NCA) with different percentages of recycled coarse aggregate (RCA) on properties of low calcium fly ash (FA)-based geopolymer concrete (GPC) cured at oven temperature. Further, this paper aims to study the effect of partial replacement of FA by ground granulated blast slag (GGBS) in GPC made with both NCA and RCA cured under ambient temperature curing.

M25 grade of ordinary Portland cement (OPC) concrete was designed according to IS: 10262-2019 with 100% NCA as control concrete. Since no standard guidelines are available in the literature for GPC, the same mix proportion was adopted for the GPC by replacing the OPC with 100% FA and W/C ratio by alkalinity/binder ratio. All FA-based GPC mixes were prepared with 12 M of sodium hydroxide (NaOH) and an alkalinity ratio, i.e. sodium hydroxide to sodium silicate (NaOH:Na₂SiO₃) of 1:1.5, subjected to 90^°C temperature for 48 h of curing. The NCA were replaced with 50% and 100% RCA in both OPC and GPC mixes. Further, FA was partially replaced with 15% GGBS in GPC made with the above percentages of NCA and RCA, and they were given ambient temperature curing with the same molarity of NaOH and alkalinity ratio.

The workability, compressive strength, split tensile strength, flexural strength, water absorption, density, volume of voids and rebound hammer value of all the mixes were studied. Further, the relationship between compressive strength and other mechanical properties of GPC mixes were established and compared with the well-established relationships available for conventional concrete. From the experimental results, it is found that the compressive strength of GPC under ambient curing condition at 28 days with 100% NCA, 50% RCA and 100% RCA were, respectively, 14.8%, 12.85% and 17.76% higher than those of OPC concrete. Further, it is found that 85% FA and 15% GGBS-based GPC with RCA under ambient curing shown superior performance than OPC concrete and FA-based GPC cured under oven curing.

The scope of the present paper is limited to replace the FA by 15% GGBS. Further, only 50% and 100% RCA are used in place of natural aggregate. However, in future study, the replacement of FA by different amounts of GGBS (20%, 25%, 30% and 35%) may be tried to decide the optimum utilisation of GGBS so that the applications of GPC can be widely used in cast in situ applications, i.e. under ambient curing condition. Further, in the present study, the natural aggregate is replaced with only 50% and 100% RCA in GPC. However, further investigations may be carried out by considering different percentages between 50 and 100 with the optimum compositions of FA and GGBS to enhance the use of RCA in GPC applications. The present study is further limited to only the mechanical properties and a few other properties of GPC. For wider use of GPC under ambient curing conditions, the structural performance of GPC needs to be understood. Therefore, the structural performance of GPC subjected to different loadings under ambient curing with RCA to be investigated in future study.

The replacement percentage of natural aggregate by RCA may be further enhanced to 50% in GPC under ambient curing condition without compromising on the mechanical properties of concrete. This may be a good alternative for OPC and natural aggregate to reduce pollution and leads sustainability in the construction.

This study reveals the green building development path and analyzes the optimal government subsidy equilibrium through evolutionary game theory and numerical simulation. This was done to explore the feasible measures and optimal incentives to achieve higher levels of green building in China.

First, the practice of green building in China was analyzed, and the specific influencing factors and incentive measures for green building development were extracted. Second, China-specific evolutionary game models were constructed between developers and homebuyers under the market regulation and government incentive mechanism scenarios, and the evolutionary paths were analyzed. Finally, real-case numerical simulations were conducted, subsidy impacts were mainly analyzed and optimal subsidy equilibriums were solved.

(1) Simultaneously subsidizing developers and homebuyers proved to be the most effective measure to promote the sustainability of green buildings. (2) The sensitivity of developers and homebuyers to subsidies varied across scenarios, and the optimal subsidy level diminished marginally as building greenness and public awareness increased. (3) The optimal subsidy level for developers was intricately tied to the building greenness benchmark. A higher benchmark intensified the developer’s responsiveness to losses, at which point increasing subsidies were justified. Conversely, a reduction in subsidy might have been appropriate when the benchmark was set at a lower level.

The expeditious advancement of green buildings holds paramount importance for the high-quality development of the construction industry. Nevertheless, the pace of green building expansion in China has experienced a recent deceleration. Drawing insights from the practices of green building in China, the exploration of viable strategies and the determination of optimal government subsidies stand as imperative initiatives. These endeavors aim to propel the acceleration of green building proliferation and materialize high-quality development at the earliest juncture possible.

The model is grounded in China’s green building practices, which makes the conclusions drawn more specific. Furthermore, research results provide practical references for governments to formulate green building incentive policies.

The purpose of this paper is to investigate cutting-edge coagulant materials and procedures for the removal of harmful microplastics from the water.

Traditional methods of removing microplastics from water bodies, like filtration, face limitations due to the small sizes involved. Hence, coagulation and flocculation emerge as essential strategies to enhance filtration efficacy. This paper summarizes recent research on coagulant materials, including novel hybrids, for water purification. It also looks at the most recent improvements in coagulation and flocculation processes, as well as the factors that influence their efficiency.

This paper highlights recent research on coagulant materials, including novel hybrids, used in water purification. It also examines the most recent advancements in coagulation and flocculation procedures, as well as the elements influencing their effectiveness.

The environmental threat posed by plastics, especially in their non-naturally degradable forms, such as microplastics, has reached alarming proportions. These minute particles pervade our air, soil and water bodies, driven by various factors and sources. Their diminutive size, whether in micro or nano form, renders them ingestible by marine and freshwater organisms, as well as humans, posing significant health risks. Traditional methods of water cleaning are not effective in dealing with very small-sized plastics and hence this paper summarizes recent research on coagulant materials, including various novel hybrids, for water purification from tiny microplastics in detail.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

This study addresses tourists' post-consumption perspectives on the impact of online destination experiences and animosity on travel decisions. Developing a framework based on the stimulus-organism-response (SOR) theory, we examine the previously unexplored relationship between post-negative events, online destination brand experience (ODBE), tourists' animosity and destination boycott intentions within the domestic tourism context.

Data from 355 actively engaged domestic travelers in Pakistan who follow destination social media pages (i.e. Instagram and Facebook) was analyzed using structural equation modeling.

The findings reveal that post-negative events ODBE significantly stimulate tourists' animosity, which in turn drives destination boycott intentions. The ODBE indirectly affects boycott intentions through animosity, acting as a partial mediator. The analysis highlights the significance of the users' prior experience levels (novice vs experienced). Multigroup analysis shows that novice visitors are more sensitive to negative online experiences, resulting in stronger animosity than experienced visitors. Animosity significantly drives boycott intentions, particularly among experienced visitors.

This study’s novelty lies in its comprehensive examination of post-negative events, focusing on how the ODBE influences tourists' negative emotions and boycott intentions. These findings offer valuable insights for tourism researchers and destination marketers, underscoring the importance of optimizing post-service failure ODBE strategies for brand repair, online reputation management, digital marketing innovation and customized service recovery to mitigate the impact of negative events.

High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications.

In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME.

The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827.

In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

The purpose of the paper is to analyze the impact of involuntary frugality and deliberate frugality on the household intentions to adopt energy-efficient and energy-generating products. Additionally, the study aims to explore the role of motivation to save as a mediating factor between different types of frugality and the adoption of different kinds of energy products.

The study involved a survey of 413 households, gathering information through questionnaires from both tier I and tier II urban areas in India. The investigation used confirmatory factor analysis and structural equation modeling with Amos to explore the impact of frugality and also mediating impacts of motivation to save on the correlation between different forms of frugality (involuntary and deliberate) and the desire to acquire energy-efficient and energy-producing goods. This methodology facilitated a thorough examination of how various levels of frugality impact the uptake of sustainable energy solutions, with a specific emphasis on the fundamental motivational drivers behind these choices.

The study uncovers specific connections between various forms of frugality and the desire to embrace energy-efficient and energy-producing items. Unintentional frugality, characterized by sensitivity to prices, is shown to have a positive correlation with the adoption of energy-efficient devices but a negative association with the intention to adopt energy-generating products. Conversely, intentional frugality, distinguished by deliberate reduction actions, positively impacts the inclination to adopt both energy-efficient and energy-generating products. The results suggest that the mediating impact of motivation for savings varies depending on the type of frugality and the class of energy products being considered, emphasizing the subtle ways in which frugality influences sustainable consumption behaviors.

The contrasting effects of involuntary and voluntary frugality on the adoption of energy-efficient versus energy-generating products highlight the need to explore the underlying psychological and economic mechanisms. Future research should investigate the factors influencing the preferences of price-sensitive and deliberate frugal consumers towards this energy-efficient and energy-generating products.

Policymakers should develop specific subsidies and financial strategies for low-income households and incentive programs for conscientious consumers. Educational campaigns emphasizing the benefits of energy-generating goods and creating incentive structures with tax advantages, refunds and financial aid are essential. Companies should continue to emphasize cost savings for energy-efficient appliances and consider leasing or instalment plans for energy-generating products to appeal to price-sensitive consumers.

Literature shows that 82% of Indians prefer frugality to conserve energy through reduced consumption. However, consumer motivations for frugality vary. This study analyses the distinct impacts of involuntary and voluntary frugality on adopting energy-efficient and energy-generating products, offering a nuanced understanding of consumer behavior in sustainability—a topic underexplored in existing research. Additionally, this study investigates the role of the motivation to save as a mediator between frugality and energy product adoption, providing a novel perspective on how different frugality motivations influence different category of energy products.