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The purpose of the study is to observe the feasibility of missing middle housing’s (MMH) realization under density-based zoning, form-based zoning and a combination of both while simultaneously providing affordable housing, improving quality of life and making efficient use of land.

This study takes a theorist approach and designs three hypothetical cottage court projects that comply with all relevant official local zoning ordinances to showcase design feasibility, followed by an analytical component in the form of a financial model constructed using official local economic and demographic conditions.

MMH, and in particular cottage clusters, can be implemented under rigorous density-based, form-based and hybrid (density-based + form-based) zoning ordinances and provide affordable housing (Atlanta, GA), improve quality of life (Blackpool, UK) and make efficient use of land (Jinan, China). All hypothetical projects are financially feasible under reasonable conditions.

To the best of the author’s knowledge, this paper is the first in the body of knowledge to discuss how the MMH can be integrated into urban density-based zoning rather than converting density-based zoning into form-based so that the MMH can fit. The paper also takes a cross-national perspective and discusses the feasibility of MMH in the resolution of housing issues in the USA, China and the UK. The study also concludes that the issue of housing unaffordability in the UK was caused by high construction cost relative to median income.

This study aims to investigate the use of 20 commonly applied regression methods to predict concrete corrosion. These models are assessed for accuracy and interpretability using SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analysis to provide structural health monitoring prognostic tools.

This study evaluated model performance using standard measures including root mean square error (RMSE), mean square error (MSE), R-squared (R²) and mean absolute error (MAE). Interpretability was evaluated using SHAP and LIME. The X and Y distances, concrete age, relative humidity and temperature were input parameters, whereas half-cell potential (HCP) values were considered output. The experimental data set consisted of observations taken for 270 days.

Gaussian process regression (GPR) models with rational quadratic, square exponential and matern 5/2 kernels outperformed others, with RMSE values around 16.35, MSE of roughly 267.50 and R² values near 0.964. Bagged and boosted ensemble models performed well, with RMSE around 17.20 and R² values over 0.95. Linear approaches, such as efficient linear least squares and linear SVM, resulted in much higher RMSE values (approximately 40.17 and 40.02) and lower R² values (approximately 0.79), indicating decreased prediction accuracy.

The findings highlight the effectiveness of GPR models in forecasting corrosion in concrete buildings. The use of both SHAP and LIME for model interpretability improves the transparency of predictive maintenance models, making them more reliable for practical applications.

Safe infrastructure is crucial to public health. Predicting corrosion and other structural problems improves the safety of buildings, bridges and other community-dependent structures. Public safety, infrastructure durability and transportation and utility interruptions are improved by reducing catastrophic breakdowns.

This study reduces the gap between model accuracy and interpretability in predicting concrete corrosion by proposing a data-driven method for structural health monitoring. The combination of GPR models and ensemble approaches provides a solid foundation for future research and practical applications in predictive maintenance. This comprehensive approach underscores the potential of data-driven methods for predictive maintenance in concrete structures, with implications for broader applications in various industries.

This study aims to investigate how digital technology influences the happiness of villagers in traditional ethnic minority communities, with Waipula Village as a focal case study. Recognized as a forerunner in achieving the United Nations Sustainable Development Goals, Waipula Village exemplifies how digital innovation can transform rural communities.

Using an exploratory case study approach, the research reveals that digital technology enhances villagers’ happiness by improving market access, mitigating geographical limitations and fostering entrepreneurship and innovation.

In addition, digital technology has led to new consumption patterns and cultural values, significantly contributing to the village’s sustainable development and overall well-being.

This analysis expands the understanding of the role of digital technology in ethnic minority villages and offers valuable insights for rural revitalization strategies, highlighting its importance in enhancing happiness and preserving cultural heritage.

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.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

This study explores the contributions of fly ash, bottom ash and biomass ash from coal and biomass power plants for enhancing circular economy of construction sectors in emerging economies.

This research investigates their applications in construction, emphasizing their role in reducing environmental impact and promoting circular economy principles. Through a qualitative analysis using data from structured interviews with 41 involved stakeholders, the study highlights the economic and environmental benefits of integrating these by-products into business operations.

Currently, the cement and concrete industries can successfully adopt almost 100% fly ash, but logistic optimization is necessary to address the wet fly ash problem. The practical applications of bottom ash pose disposal challenges due to their poor adoption. Biomass ash can be alternatively implemented as a soil amendment and fertilization in the agriculture industry while current growth seems significant with the shift to a clean energy policy.

This research underscores the importance of policy support and collaboration between industry stakeholders to maximize the sustainable potential of these by-products in an emerging economy context.

The sustainability development goals (SDGs) were well-established in developing economies. Nevertheless, the literature review indicates that there is a lack of understanding regarding their backgrounds, influencing factors, challenges and practical applications for the circular economy.

In order to improve the rationality of the design of carbon fiber composite riveting structures in engineering products and reduce physical tests, the stress changes of various parts during the carbon fiber-reinforced polymer (CFRP) riveting process, the influence area of hole edge stress and the damage of CFRP plates were studied from the perspective of numerical simulation. The reasonable arrangement requirements of composite riveted structures, installation speed and damage characteristics of CFRP plates in engineering applications are obtained. The research results provide technical references for the design and installation of composite riveting structures in engineering products.

Taking the forming process of the riveted structure between countersunk blind rivets and CFRP plates as the research object, the forming principle of countersunk blind rivets and the damage characteristics of CFRP plates were analyzed. Using contact nonlinearity theory, the anisotropic material modeling method and the Hashin failure criterion, the stress changes in various parts during the riveted joint forming process, the influence area of stress at the hole edge of the rivet holes and the damage of the CFRP plates were analyzed.

Reasonable rivet layout spacing was obtained; the peak stress of the rivet assembly increases with the increase of the rivet installation speed, and the influence area of the hole edge on the CFRP plate is a circular area with an outward extension radius of 0∼6 mm. Therefore, the arrangement distance between rivets is greater than the ring area, the damage law and installation speed of CFRP plates are given, the damage extends inward from the first layer of the laminates and the installation speed of 15 mm/s can satisfy the requirement of riveting installation better.

The riveting and forming process under different speeds is analyzed from the perspective of numerical simulation, and the stress variation rule of each part of the rivet and the damage of the CFRP plate under each speed are obtained. Reasonable rivet arrangement requirements and installation speed were given. This study provides technical support for the rivet arrangement method and mechanical property analysis of CFRP riveted structures in complex engineering products.

This study aims to enhance our understanding of employee emotional competence (EEC) in the context of service failure and recovery. Accordingly, the present study investigates the relationship between perceived EEC and customer emotional attachment (CEA) through the mediating role of service recovery satisfaction (RES). Furthermore, the study examines the moderating impact of service failure severity (SFS) on the relationship between perceived EEC and RES.

A self-administered online survey was carried out to collect data. Using a convenience sampling technique, 195 US consumers were recruited from Prolific Academic. To test the hypotheses, this study employed partial least squares structural equation modeling (PLS-SEM).

According to the analysis, perceived EEC impacts CEA directly and indirectly via RES. Additionally, the study finds that consumers reported feeling more emotionally connected to the restaurant when they were satisfied with service recovery. Finally, the study identified that the connection between perceived EEC and RES increases with service failure severity.

This study emphasizes enhancing EEC through organization-wide training to increase customer satisfaction and emotional attachment to the service organization. Furthermore, it underscores the need for comprehensive employee training to categorize service failure severity and formulate appropriate recovery strategies.

The authors believe this is the first RES study to examine perceived EEC’s effect on CEA. By combining the affect infusion and cognitive appraisal theories to examine recovery satisfaction, this study contributes to the existing body of research on service recovery by shedding light on the relationship between perceived EEC and CEA. Furthermore, the study offers preliminary findings indicating an increase in the impact of perceived EEC on RES during high failure severity (SFS).

This chapter explored health and safety considerations in stealth construction, emphasising the integration of advanced technologies and innovative practices. It commences with a general introduction, followed by a historical overview of safety practices in the construction industry, highlighting the evolution of a safety culture. The chapter examined various health and safety management techniques, including policy formulation, safety training programs, and job safety analysis. Additionally, it discussed current trends such as wearable technology, IoT, VR/AR, and predictive analytics. The unique requirements of stealth construction are addressed, focusing on building cross-section design, visibility, application of radio frequency emission and countermeasures. Finally, it presents a comprehensive approach to achieving stealth construction, emphasising environmental protection, safety, speed, economy, and aesthetics, and provides practical examples to illustrate these concepts.

This paper aims to identify modern construction techniques for affordable housing, such as prefabrication and interlocking systems, that can save time and cost while also providing long-term sustainable benefits that are desperately needed in today's construction industry.

The need for housing is growing worldwide, but traditional construction cannot cater to the demand due to insufficient time. There should be some paradigm shift in the construction industry to supply housing to society. This paper presented a state-of-the-art review of modern construction techniques practiced worldwide and their advantages in affordable housing construction by conducting a systematic literature review and applying the backward snowball technique. The paper reviews modern prefabrication techniques and interlocking systems such as modular construction, formwork systems, light gauge steel/cold form steel construction and sandwich panel construction, which have been globally well practiced. It was understood from the overview that modular construction, including modular steel construction and precast concrete construction, could reduce time and costs efficiently. Further enhancement in the quality was also noticed. Besides, it was observed that light gauge steel construction is a modern phase of steel that eases construction execution efficiently. Modern formwork systems such as Mivan (Aluminium Formwork) have been reported for their minimum construction time, which leads to faster construction than traditional formwork. However, the cost is subjected to the repetitions of the formwork. An interlocking system is an innovative approach to construction that uses bricks made of sustainable materials such as earth that conserve time and cost.

The study finds that the prefabrication techniques and interlocking system have a lot of unique attributes that can enable the modern construction sector to flourish. The study summarizes modern construction techniques that can save time and cost, enhancing the sustainability of construction practices, which is the need of the Indian construction industry in particular.

This study is limited to identifying specific modern construction techniques for time and cost savings, lean concepts and sustainability which are being practiced worldwide.

Modern formwork systems such as Mivan (Aluminium Formwork) have been reported for their minimum construction time which leads to faster construction than traditional formwork.

The need for housing is growing rapidly all over the world, but traditional construction cannot cater to the need due to insufficient time. There should be some paradigm shift in the construction industry to supply housing to society.

This study is unique in identifying specific modern construction techniques for time and cost savings, lean concepts and sustainability which are being practiced worldwide.