Architecture, Structures and Construction最新文献

The construction sector faces the daunting task of meeting growing construction demand with a 'zero-emission resource pool'—materials that are compatible with a near-future zero-emissions economy. Most decarbonisation roadmaps and scenario analyses for the sector depend heavily on high-risk technologies such as carbon storage that have not yet been deployed at significant scale, or favour recycling whilst overlooking likely constraints from limited supplies of zero-emissions electricity. This paper therefore provides a first critical review of options to supply construction materials in the UK with realistic expectations about the availability of carbon storage, zero-emissions electricity and zero-emissions transport. The paper focuses on nine key construction materials—concrete, steel, aluminium, structural glass, timber, earth, stone, lime and straw. We conclude that the zero-emissions resource pool includes virgin bio-based materials, limited by the availability of productive land, virgin earth and stone, limited by local geology and transportation, recycled materials, limited by the availability of scrap and emission-free electricity, and reused components, limited by availability and refurbishment potential. This points to the need for a revision to the national construction strategy and a range of entrepreneurial opportunities in delivering the services of construction within a reduced material budget.

Manufacturing Portland cement, the second most widely used material after water, is a highly energy-intensive process that contributes to 8–10% of global CO2 emissions. With the rising demand for construction materials, the search for sustainable alternatives has become imperative. This study examines rice husk ash (RHA)-based concrete as a promising alternative to Portland cement, highlighting its significantly lower carbon footprint and improved mechanical properties. Utilizing agricultural by-products such as rice husk, this research investigates the effects of various factors, including concrete age, superplasticizer dosage (ranging from 6.2 to 7.36 kg/m³), fine aggregate content (1819 to 1859 kg/m³), and RHA (55 to 100 kg/m³), on the compressive strength of RHA-based concrete across 186 different mix designs. Five modeling techniques Linear Regression, Non-Linear Regression, Multi-Linear Regression, Artificial Neural Network (ANN), and M5P-Tree were employed to predict compressive strength, ranging from 16 to 104.1 MPa. Model performance was evaluated using metrics including correlation coefficient, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), and Objective Function (OBJ). The results indicated that the ANN model outperformed all other techniques, exhibiting superior predictive accuracy and minimal residual error. Sensitivity analysis revealed that age, superplasticizer, fine aggregate, and RHA content were the most influential factors on compressive strength. This research underscores the significant potential of RHA-based sustainable concrete as an eco-friendly alternative to traditional Portland cement, paving the way for more sustainable construction practices.

This study presents a predictive framework for estimating the compressive strength of preplaced aggregate concrete (PAC) using a comprehensive dataset and advanced statistical modeling. A total of 261 concrete mix samples were compiled, each incorporating various combinations of materials such as cement, fly ash, silica fume, GGBS, sand, gravel, water, superplasticizer, and expanding admixtures. Key mix design parameters like the water-to-binder (W/B) and sand-to-binder (S/B) ratios were systematically varied to reflect realistic construction practices. To identify the most influential components and improve model performance, data normalization and sensitivity analysis were performed. The analysis revealed that the W/B ratio was the most critical factor, contributing approximately 31.5% to compressive strength variation. The independent variable ranges in the dataset are as follows: cement (176–873 kg/m3), fly ash (0–262 kg/m3), silica fume (0–57 kg/m3), GGBS (0–228 kg/m3), sand (0–873 kg/m3), water (100–431 kg/m3), gravel (1.5–2001 kg/m3), water to cement ration (W/B) ranged between 0.3–0.85, S/B (0–2), superplasticizer (0–10.9 kg/m3), and expanding admixture (0–58.6 kg/m3). Compressive strength, the dependent variable, ranged from 5.7 MPa to 58.6 MPa. Sensitivity analysis identified W/B as the most influential variable, showing a sensitivity of 31.5% across samples. After testing multiple models, the Full Quadratic (FQ) model emerged as the most accurate based on RMSE, MAE, and OBJ performance criteria. The strength values ranged from 5.7 MPa to 58.6 MPa, encompassing low- to high-strength concrete applications. Among several tested models, the Full Quadratic (FQ) model demonstrated the highest prediction accuracy based on key evaluation metrics (RMSE, MAE, and objective function). This model offers a reliable tool for engineers to estimate compressive strength and optimize mix design without extensive laboratory testing. The proposed approach contributes to reducing construction costs, enhancing design efficiency, and supporting data-driven decision-making in sustainable concrete development.

This study was devoted towards an experimental investigation to evaluate the effectiveness of a newly proposed strengthening technique for flexure deficient RC beam. In the proposed technique, the beam was strengthened using readily available mild steel (MS) flat bar and royal bolt to make the process quick and economic. The effects of various important factors viz., the number of bolt and its diameter, thickness and size of flat bar and their location were investigated using four-point bending test. Test results revealed that the proposed strengthening technique is effective enough to enhance the load carrying capacity of the control beam in flexure. The yield and ultimate strengths increased by as much as 45% compared to the control beam in one of the strengthened cases. It was found that the size and thickness of MS flat bar and its location have noticeable influence on the flexure capacity of the strengthened beam. The anchorage failure of royal bolt was the dominant mode of failure, and the effectiveness of the method mainly depends on the performance of the royal bolt anchorage. The royal bolt failure could be avoided by providing enough number of royal bolts to utilize the full tensile capacity of the MS flat bar and improve the flexure behavior of the strengthened beam.

The absence of clear standards in the design of public buildings has resulted in structures that are deficient in both visual appeal and cultural relevance, which has lead to the customisation of public buildings. However, there are several factors responsible for the nature of cosmetic customization that individual building owners or tenants initiate in the building. This paper, therefore, aims to examine the factors responsible for cosmetic customization in public buildings to determine if there is any significant relationship between the nature of the building and the factors responsible for the modification. A mixed-method approach was adopted for this study with the use of a questionnaire and observation checklist. A total of 330 respondents were selected from the 11 categories of public buildings, and the data obtained was analyzed using descriptive statistics from SPSS. The results indicate that there is a significant relationship between the nature of the public building and the factors responsible for the modification undertaken on the building. Based on these findings, the paper concludes that the customization of public buildings plays a crucial role in enhancing their functionality, user experience, and overall effectiveness. To effectively address the needs and expectations of users, it is imperative to consider the nature of the service provided within the building.

Train-induced vibrations can negatively affect nearby structures and human comfort, yet traditional methods of assessing vibrations are either complex or limited. This research aims to provide a user-friendly, coding-free machine learning (ML) tool to accurately predict these vibrations, empowering civil engineers to conveniently implement advanced ML approaches in practical applications. Using XLSTAT, a coding-free ML platform integrated into MS Excel, multiple regression algorithms were tested. The Extreme Gradient Boosting (XGBoost) algorithm was selected based on superior predictive accuracy. Two datasets were analyzed: Dataset-1, obtained from published literature, and Dataset-2, collected experimentally at Shalimar Gardens, a UNESCO World Heritage site. Predictions were compared with results from the empirical Federal Transit Administration (FTA) method. XGBoost significantly outperformed the empirical FTA method. For Dataset-1, XGBoost achieved an R² of 0.9092 compared to 0.8618 from FTA. For Dataset-2, XGBoost notably excelled with an R² of 0.9622, whereas the FTA method reached only 0.1907. Additionally, the XGBoost algorithm demonstrated higher accuracy than the previously used GRNN and BPNN models from the literature. A user-friendly, coding-free ML approach using XLSTAT effectively predicts train-induced vibrations with high accuracy, substantially surpassing traditional empirical methods. This accessible tool facilitates practical vibration prediction and supports civil engineers in making informed, data-driven decisions to mitigate adverse impacts of structural vibrations.

Historic buildings require risk management as they are exposed to potential damage in the event of future hazards and disasters. The aim of this study is therefore to clearly identify and classify risks and determine their extent and vulnerability in historic buildings through the development of a holistic risk management strategy in order to contribute to their preventive conservation by proposing improvement plans before they become hazards. Four museum cases were selected as examples of historical buildings in Konya. The potential risks threatening the buildings were determined using data from national databases and the ABC risk analysis method developed by the Canadian Conservation Institute (CCI) and the International Centre for the Study of the Preservation and Restoration of Cultural Property (ICCROM) is used as a risk management approach. In addition, field research, oral interviews, archival and literature research were conducted. By combining all data, a holistic methodology was developed to identify potential risks threatening the buildings, their vulnerability and priority levels, and to determine the current condition of the buildings/museum collections to understand the need for preventive conservation and restoration. As a novel contribution, this study engages in comparative studies by analyzing buildings from different eras, with different materials and construction techniques, and proposes a systematic framework for heritage risk management that combines both qualitative and quantitative approaches. According to the results of the analysis, the average risk of the buildings was categorized as medium and their priority level as low. The analyses carried out were consistent with the results of the on-site observations, so the reliability of this method was considered high. In addition, the vulnerability and priority levels determined by the risk analyses and on-site observations provided accurate information on the current condition of the buildings.

This study investigates the integration of Agile management principles—specifically a Scrum-based framework—into the design phase of construction projects within a Building Information Modeling (BIM) environment. Recognizing that traditional approaches (e.g., the linear “Design-Bid-Build” method) constrain collaboration and flexibility, the research proposes a modified framework designed to enhance stakeholder communication, reduce rework, and achieve greater design accuracy. The methodology combines a focused literature review with qualitative data from structured interviews with experienced construction engineers. Key findings reveal that adopting Agile principles in the design stage can significantly reduce scope creep, optimize resource utilization, and improve client satisfaction. Limitations and avenues for future research, including applications during implementation and supply chain management, are also discussed.

The production, conversion, and utilization of energy are central contributors to global environmental challenges. Among the various renewable energy solutions, solar energy has emerged as a promising and scalable option. The efficiency of photovoltaic (PV) systems, especially in building applications, is highly dependent on the configuration of the panels—particularly their tilt angle. This study investigates the optimal tilt angles for PV panels installed on the south-facing façades of buildings in Babolsar, Iran, with the goal of maximizing annual solar radiation reception and energy yield. Using the Liu and Jordan isotropic sky model, combined with meteorological data and custom MATLAB simulations, monthly, quarterly, and fixed-angle strategies were analyzed. A system capacity of 1.5 m² panel area was assumed, with annual energy output ranging from approximately 336 kWh/year for a flat (0°) installation to 377 kWh/year with monthly tilt adjustment. Economically, the quarterly strategy offered the highest return on investment (ROI), with an estimated payback time of 7.8 years and a levelized cost of electricity (LCOE) of approximately $0.083/kWh. Environmentally, the optimized system reduces CO₂ emissions by approximately 180 kg/year compared to conventional grid-based electricity. A fixed optimal tilt angle of 30° was also found to be a practical, low-maintenance alternative. These findings provide both technical and environmental insights for enhancing the efficiency and sustainability of solar energy systems in Babolsar and similar climates.