China’s chemical manufacturing sector has experienced remarkable growth in recent years, making it a global leader in chemical production. However, this rapid expansion has led to an increase in chemical accidents, particularly major chemical accidents (MCAs), resulting in significant casualties and property loss. This study focuses on MCAs that occurred in China between 2017 and 2022, using mathematical statistics. It examines various aspects of accidents, including the annual number of accidents and casualties, distribution across months, types of accidents, accident stages, direct causes of accidents, and geographical distribution of accidents. Furthermore, this study investigated the potential of using novel digital tools to enhance the safety of chemical production. By analysing the data and identifying trends, this study aims to contribute to the prevention of large-scale chemical disasters. Furthermore, it explores the implementation of smart management of chemical plants, utilising the Internet of Things (IoT) for example, to ensure the sustainable advancement of the chemical industry.
Construction productivity research has exploded in the twenty-first century, captivating scholars worldwide. To navigate this burgeoning field, this study utilizes a scientometric analysis approach to identify and evaluate 710 academic articles, examining geographical publication patterns, author contributions, leading journals, keyword co-occurrences, and key findings from previous studies. The results reveal that the United States, Canada, and Australia are the top contributors in terms of publication output. The Journal of Construction Engineering and Management, Automation in Construction, and Construction Management & Economics emerged as leading journals. Keyword analysis finds “productivity,” “construction industry,” and “project management” to be the most prevalent. Notably, research relies on empirical methods like questionnaires and utilizes popular measures such as relative importance index, factor analysis, and regression analysis. Additionally, smart construction and sustainable cities appear as promising paradigms for achieving sustainable productivity. Furthermore, prior studies advocate for workforce upskilling, enhanced motivation, work environment improvements, strengthened site management, and embraced technological advancements to boost construction productivity. This paper enriches the existing body of knowledge by mapping the global research landscape on construction productivity, uncovering emerging trends, identifying influential contributors, and highlighting promising areas for future research. In practical terms, it provides construction practitioners with valuable insights into emerging technologies and promising management approaches that can enhance productivity and optimize construction processes.
Due to global warming, considerable amounts of storm rain have occurred, causing urban water logging and flooding. The efficient scheduling of drainage systems among pumping stations is crucial to mitigating flash flooding in urban areas. This study introduces a Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm as a proactive solution for addressing urban flooding through the optimization of drainage system discharge capacities. The algorithm's robustness is guaranteed through the integration of a multi-tier drainage system and dependency relationships. Additionally, the incorporation of an importance parameter is considered for facilitating the practical exploration of flooding risk evaluation. The proposed model was applied to simulate a drainage system in Haining City, and the results indicate that its accuracy, flexibility and reliability outperform that of existing algorithms such as fixed-priority scheduling. Moreover, the proposed approach enabled a considerable reduction in overflow loss and improved the efficiency of the sewage system. This method can improve the responses of cities to the rising problem of urban water logging.
There are many incidents involving scaffolding which might lead to serious occupational safety consequences. A poor scaffold erected along public access in Hong Kong is taken as an example to alert the hidden hazards. This indicates inadequacy in safety management with possible violation of the Occupational Safety and Health Ordinance and possible violation of at least three local fire codes of practice, including Means of Escape, Means of Access and Fire Service Installations Codes. Blocking public access for fire exits would lead to hazardous consequences. Occupants might not be able to escape safely and efficiently, and firemen might not be able to access the fire spot quickly. These would lead to unnecessary injury and property damage, court case, insurance claim, and so on. It is obvious that fire safety management should be implemented properly to ensure proper monitoring of the scaffold. However, current control appears to be too loose. Safety culture should be promoted by stakeholders with tighter government control, inspection and penalty. The new penalty scheme of the Occupational Safety and Health Ordinance is a good starting point.
Current design Standards for reinforced concrete beams prescribe to respect a minimum, ρ min, and a maximum, ρ max, reinforcement ratio in the design of structures. Below ρ min a brittle failure due to unstable crack propagation is expected. On the other hand, for ρ > ρ max a brittle failure due to concrete crushing is obtained. In this framework, a reinforced concrete element with ρ min < ρ < ρ max presents yielded steel at Ultimate Limite State (ULS) with a stable behaviour and no catastrophic loss of bearing capacity. Design Standards define ρ min and ρ max limits on the basis of the Bernoulli’s hypothesis of plane sections, and completely disregard size-scale effects. Within the present paper, Dimensional Analysis is used to determine the Brittleness Numbers that govern the behaviour of reinforced concrete (RC) as well as of prestressed reinforced concrete (PC) beams. Therefore, parametric analyses carried out by means of the Cohesive/Overlapping Crack Model (COCM) are used to study the ductile-to-brittle transitions in RC and PC beams, and to highlight the size-scale dependency of the two above-mentioned reinforcement limits.
In this study, we investigated the application of the deep mixing method (DMM) to cohesive soil in the Saga Lowland of Kyushu, Japan. The study focused on examining three types of water-cement ratio (W/C) conditions, with a constant addition of cement-based binder (C). Due to the soft clay nature of the Saga Lowland, frequent ground settlement and deformation occur, necessitating measures to prevent adverse effects on the surrounding environment. The objective of this research is to provide a valuable approach to optimizing the quality of the improved columns while minimizing ground displacement in an environmentally considerate manner. In Saga Lowland, it is common to fix the W/C ratio at 1.0 and vary the cement content. Through experimental construction for improved columns on the field, the study confirmed that W/C values of 0.5, 1.0, and 1.5 influence the quality of the improved structure. A higher W/C value of 1.5 resulted in a more fluid cement slurry due to a higher injection rate (IR = 23.9%), as evidenced by statistical analysis revealing higher average unconfined compressive strength ( $\overline{{q }_{u}}$), and a lower coefficient of variation (CV). The defective rate of 10% (q udr ) from the design standard strength shows that values are lowest for Case 2, followed by Case 3 and then Case 1. Comparing the values of Case 2 and Case 3, it is observed that in Case 3, with a higher W/C, the CV is lower.. Regarding horizontal ground displacement (Sh), Case 3 exhibited a Sh value of 2.0 to 6.5 mm, significantly lower than Saga prefecture standards (20 mm). This outcome is attributed to reduced viscosity during mixing, leading to improved fluidity and minimal lateral displacement of the soil–cement columns (which often results in lateral ground uplift). Even though with a higher W/C = 1.5, the implementation cost remains the same, but the constructed structure would be of higher quality and smaller displacement, with the overall structure corresponding to the standard quality. The study includes the specific geotechnical conditions of the Saga Lowland and the scope of experimentation. Nonetheless, in terms of the applicability and optimization of DMM in Saga Lowland, the findings provide practical guidance for engineers in selecting W/C ratio and IR during construction for future DMM implementations, thereby contributing to the development of long-lasting infrastructure and sustainable societal development.
The widespread use of concrete has raised concerns about the consumption of its raw materials. To address this issue, researchers are actively exploring alternative waste materials to reduce the environmental impact of the construction industry. With this objective current study aims to investigate potential use of waste polyethylene terephthalate (PET) bottle cap aggregates on the characteristics of concrete, both in its fresh and hardened states. Three variations of modified waste PET aggregates (PETA): full (F), half (H), and quarter (Q), with the aim of evaluating their suitability as replacements for conventional coarse aggregates (CCA) was examined. The research assesses the effects of different PETA proportions (3%, 5%, and 8%) on concrete workability, compressive strength, splitting tensile strength, and flexural strength. The findings reveal that PETA-F and PETA-H adversely affect workability, primarily due to their larger surface area and difficulty in achieving homogeneous blending with other ingredients. In contrast, PETA-Q exhibits superior workability. The use of PETA-Q results in reduced compressive strength due to its smaller size and limited load-bearing capacity. Notably, PETA-H demonstrates improved compressive strength after 28 days. Interestingly, PETA-Q shows enhancements in splitting tensile and flexural strength, making it the top-performing alternative for fresh and hardened concrete properties. Overall, this research indicates that PETA-Q is most suitable for sustainable concrete production. Furthermore, the application of a multi-criteria decision-making tool validates our findings and determines that a 5% replacement of CCA with PETA is optimal, as higher replacements result in strength reduction. The utilization of PETA not only minimizes CCA consumption but also contributes to the development of eco-friendly, sustainable concrete. This study underscores the potential of environmentally conscious concrete and encourages the adoption of sustainable solutions for plastic waste management.
Assessing the impact of river water on concrete properties is an important aspect of the construction industry, especially in regions with significant exposure to riverine environments, like Bangladesh. This study investigated the applicability of river water in concrete manufacturing regarding its compressive strength and density. Water samples were collected from two major rivers (the Padma and the Shitalakshya) in Bangladesh and analyzed for thirty water quality parameters. 168-cylinder specimens were cast and tested for compressive strength after curing for 7, 14, and 28 days. The investigation was also carried out with two different coarse aggregates (brick and stone chips) in the mix design. Curing was performed with fresh and river water separately. Therefore, ten different experimental conditions were explored. The compressive strength of concrete using river water compared to freshwater decreased from 0 to 24%, except for three cases, where strength increased by 4.2%, 7.3%, and 8%. The variation in water quality between the two rivers significantly influenced the reduction in compressive strength. Higher values of pH, total suspended solids, total solids, ammonia, total hardness, alkalinity, and conductivity in the Padma River water led to a greater reduction in compressive strength than using the Shitalakshya River water. The type of coarse aggregate used also had an impact on strength. Both rivers’ water met the concrete production standards; nevertheless, a pre-treatment process is recommended. Successfully using river water in concrete production could reduce the demand for freshwater resources, contributing to the construction industry’s sustainability.
Traditional methods for evaluating the quality of the Deep Mixing Method (DMM) in Japan, such as the phenolphthalein tracer method, present limitations in ensuring strength improvement. This study explores handheld X-ray fluorescence (XRF) as a sustainable alternative for quality assessment. The elemental composition of cement-treated soil was investigated, focusing on Calcium (Ca), Silicate (Si), and Sulfate (S), which play crucial roles in the formation of hydrated products, aiming to accurately determine their influence on the strength development of the improved soil. A controlled protocol was employed using commercial Kaolin clay and the needle penetration test for strength assessment. Our laboratory experiments, conducted with a cement-based binder (C) dosage of 110 kg/m3 and a water-cement (W/C) ratio of 1, furthered our understanding of the hydration process. Results indicate an apparent increase in Ca amount over time, correlating with improved strength, while the apparent amount of Si decreases, suggesting its integration into hydration products such as C-S–H gel. Strength assessment rose significantly between day 1 and 28, aligning with variations observed in Si, Ca, and S, underlining their role in strength evolution. This research underscores the potential of handheld XRF as a sustainable substitute for conventional methods in field-quality assurance, offering real-time data on the formation of possible hydrated products during the hydration process for improved soil in geotechnical engineering.
Digital visual data, such as images and videos, are valuable sources of information for various construction engineering and management purposes. Advances in low-cost image-capturing and storing technologies, along with the emergence of artificial intelligence methods have resulted in a considerable increase in using digital imaging in construction sites. Despite these advances, these rich data sources are not typically used to their full potential because they are processed and documented subjectively, and several valuable contents could be overlooked. Semantic content analysis and annotation of the images could enhance retrieval and application of the relevant instances in large databases. This research proposes an ensemble approach to use deep learning-based object recognition, pixel-level segmentation, and text classification for medium-level (ongoing activities) and high-level (project type) annotation of still images from various outdoor construction scenes. The proposed method can annotate images with and without construction actors, i.e. equipment and workers. The experimental results have shown the potential of this approach in annotating construction activities with an 82% overall recall rate.
Making an effective traffic strategy is a challenging problem that involves efficiency and safety, where traffic lights play an important role. Especially in cases without traffic lights, creating traffic strategies benefits significantly more complex and challenging. This complexity underscores the importance of research on autonomous vehicles, which has been trained widespread attention in recent years. In order to avoid these potential crises, the designed traffic strategy at traffic light-free intersections must be effective and reliable, ensuring the basic processes work properly. In this paper, we propose a novel method to formally model the traffic strategy for vehicles to make sure they safely and orderly pass through traffic light-free intersections. We use the state space analysis method to analyze the vehicle traffic model to ensure the decision made at each step is correct. By using Petri net, our proposed model achieves excellent analytical capabilities for asynchronous or highly concurrent systems than simpler Unified Modeling Language (UML) systems, as well as the ability to dynamically analyze system security and reliability.
Advanced structural design approaches should consider the economic and technological benefits offered by the structural applications of fibre-reinforced concrete. In this framework, it is important to highlight how the ductility of fibre-reinforced concrete structures is strongly dependent on the fibre volume fraction together with the structural size. This crucial coupling induces two reverse ductile-to-brittle transitions in the mechanical response of fibre-reinforced and hybrid-reinforced concrete elements: by increasing the characteristic size of the structure, an increase in its load-bearing capacity can be observed together with a decrease in its plastic rotation capacity. These size-scale effects can be taken into account by an effective fracture mechanics approach represented by the Updated Bridged Crack Model (UBCM), which can provide significant improvements in current Standards and regulations on fibre-reinforced concrete structures.
In order to preserve the natural fine aggregates and proper waste resources recycling, rice husk and eggshell powders were used as filler materials in production of standard M30 grade mortar. Natural fine aggregate was replaced with rice husk powder (RHP) at 3%, 5%, 7%, 10%, 12%, and 15%, and with eggshell powder (EP) at 3%, 5%, and 7%, both at a constant water-to-cement ratio of 0.40. Compressive strength, split tensile strength, water absorption, pore distribution, and electrical resistivity of the mortar samples were investigated. Also, XRD, SEM, and EDS were performed to observe the samples’ chemical phases and microstructure. The results show that up to 15% RHP reduced the compressive strength by 20–35%, whereas the up to 7% EP obtained a similar (varies by 0–5%) result compared to the control sample at 28 days. Furthermore, the results suggest that sand replacement with RHP and EP had no impact on the water absorption and pore volume of mortars. Also, the samples made with up to 15% RHP and 7% EP improved the resistivity by up to 80% and 15%, respectively, providing better resistance to chloride ion migration. Based on the results, it is recommended to use the RHP and EP up to a level of 5% and 7%, respectively, in sustainable M30 grade mortar production.
The analytical hierarchy process (AHP) and frequency ratio model (FR), along with the integration of GIS, have proven to be successful approaches for assessing flood-prone areas. However, in Nepal flood vulnerability mapping based on GIS decision analysis is limited. Thus, this study focused on comparing the data-driven FR method and expert knowledge-based AHP technique in a GIS environment to prepare a flood vulnerability map for the Bagmati River basin, helping to explore the gap in flood vulnerability mapping methodologies and approaches. By combining all class-weighted contributing factors, like elevation, precipitation, flow accumulation, drainage density, soil, distance from the river, land use land cover, normalized difference vegetative index, slope and topographic wetness index, the study evaluated the efficiency of FR and AHP in assessing flood vulnerability maps. An inventory map of floods containing 107 flood points was created. Subsequently, the flood vulnerability maps generated using FR and AHP models revealed that 9.30% and 11.36% of regions were in highly vulnerable areas, respectively. Receiver operating characteristics validated the model outcomes, indicating that the FR model’s accuracy of 91% outperformed the AHP model’s 84% accuracy. The study findings will assist decision-makers in enacting sustainable management techniques to reduce future damage in the Bagmati basin.
In our society, global warming is considered one of the most serious problems. According to scientists, the world has been warmed by 3 degrees per year, which will be catastrophic to our world. To reduce CO2 emission, an electric bus is one way to solve the problem. In this article, we use four different models: Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average Model (ARIMA), Ecological Assessment Model, Bus Fleet Replacement Financial Model, and Integer Programming Model to determine the number of carbon emissions, the least money that government need to spend on transitions, and future blueprint; these help to predict the overall benefits for countries turn into absolutely electric bus society. Our research stands from the sustainable point of view; we view better environment as the goal. By applying these models to three different countries: London, and Toronto, and Philadelphia which is our main focus, we find out that the air quality will be increased by reducing different kinds of pollution. Moreover, by constructing a ten-year blueprint, we find out the best way to spend least money and make the environment gradually become better.