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The research investigates the effects of substituting sand with rubber particles derived from waste tyres—up to 40% by volume—and the inclusion of polypropylene (PP) fibres. Unlike steel fibres, which can cause operational challenges and surface irregularities in the printing process, PP fibres' flexibility integrates well within the concrete matrix. This integration ensures smooth extrusion and a high-quality surface finish, enhancing the printability of the concrete. The study's findings reveal that including rubber particles and PP fibres impacts the concrete's properties, showing a general decline in compressive and flexural strengths as the rubber content increases. Nevertheless, the PP fibre–enhanced mixtures maintain sufficient structural strength, demonstrating an anisotropic compressive strength above 30 MPa and a flexural strength of 4 MPa. These results underscore the feasibility of using rubberised 3D-printed concrete with PP fibres in sustainable construction practices, aligning with standards (ACI 318:2018) and contributing to eco-friendly and innovative construction methodologies. [ABSTRACT FROM AUTHOR]

This study is aimed at the fact that material allocation and construction progress cannot be intelligently controlled in the construction of prestressed steel structures. An intelligent planning method of a material-allocation path for prestressed steel-structure construction, based on digital twins (DTs), is proposed. Firstly, the characteristics of material allocation in the process of structural construction are analyzed, and a five-dimensional integrated DT framework for intelligent path-planning is built. Driven by the DT framework, the progress and environmental information of the construction site are collected in real time. At the same time, the field working conditions are dynamically simulated in the virtual model, so as to realize the interactive mapping between physical space and virtual space. In each construction process, by integrating the progress of each process at each construction location, and the storage and allocation of materials, a multidimensional model for the intelligent planning of material allocation is formed. The information fusion of virtual and real space is carried out using an entropy method to analyze the construction buffer time and material allocation time at each location of the construction site. On this basis, combined with the Dijkstra algorithm, the transportation time associated with the path is calculated according to the field distribution of each location. A feasibility analysis is carried out in the virtual model and imported into the field dynamic-marking system. Combined with radio frequency technology to guide material allocation on site, the intelligent planning of the material-allocation path is realized. In this study, taking the construction of the National Speed Skating Pavilion of the 2022 Beijing Winter Olympics as an example, the DT technology and Dijkstra algorithm are applied to the intelligent planning of the material-allocation path. It is fully verified that the intelligent method can effectively coordinate the relationship between schedule control and material allocation. [ABSTRACT FROM AUTHOR]

Electrically conductive cementitious composite (ECCC) can be utilized in traffic detection, structural health monitoring (SHM), and pavement deicing. Graphite is a popular conductive filler given its outstanding electrical resistance behavior, but its flat micro‐surface decreases the friction resistance, reducing mechanical strength. However, slag solids can substitute part of the graphite with their considerable conductivity and superior mechanical characteristics. In this research, 27 ECCC composites are developed incorporated by combined graphite powder (GP), blast furnace slag (GGBS), and steel slag (SS). Flexural, unconfined compressive strength (UCS) experiments, and resistance experiments with failure mode evaluations are carried out to explore the influence of proposed conductive fillers with different fractions. Results show the graphite strongly improves conductivity but dramatically reduces mechanical performance. Both steel slag and GGBS reduced the compressive and flexural strength after their replacement ratio exceeded 20%. Additionally, compared with GGBS, SS demonstrated a better influence on mechanical and conductive performance. A 15 wt% ratio of GGBS, 20 wt% of SS, and 4 wt% of GP are explored as the optimum design with 3.4 MPa of flexural strength, 36 MPa of compressive strength, and 7,779 Ω cm of resistance. Lastly, a microstructural investigation is conducted to explore the mechanical and conductive mechanism of ECCC. [ABSTRACT FROM AUTHOR]

In this research, the plate embedded parts and grooved embedded parts reinforced concrete structures were investigated. Two types of plate embedded parts and three types of grooved embedded parts experienced coating treatment to enable sustainable function. Later, the ultimate failure capacity by bending experiments was conducted and compared with the theoretically calculated results. Moreover, three grooved embedded parts were simulated by ABAQUS to compare the results with the experimental exploration results, which was in close agreement with the theoretically calculated results and finite element analysis results. The result indicated that the failure modes of the embedded specimens under the five working conditions are all concrete vertebral failure. The plate-type embedded components were proved to exhibit higher ultimate bearing capacity than the grooved embedded parts. Moreover, the flexural and shear capacity of these five types of embedded parts has not been fully developed. The ultimate flexural and shear capacity of these five types of embedded parts could be further explored by adjusting the higher concrete grade. [ABSTRACT FROM AUTHOR]

The roadway stability has been regarded as the main challenging issue for safety and productivity of deep underground coal mines, particularly where roadways are affected by coal mining activities. This study investigates the −740 m main roadway in the Jining No. 2 Coal Mine to provide a theoretical basis for the stability control of the main deep roadway affected by disturbances of adjacent working activities. Field surveys, theoretical analyses, and numerical simulations are used to reveal mechanisms of the coal mining disturbance. The field survey shows that the deformation of roadway increases when the work face advances near the roadway group. Long working face mining causes the key strata to collapse based on the key strata theory and then disturbs the adjacent roadway group. When the working face is 100 m away from the stop-mining line, the roadway group is affected by the mining face, and the width roadway protection coal pillar is determined to be about 100 m. Flac3D simulations prove the accuracy of the theoretical result. Through reinforcement and support measures for the main roadway, the overall strength of the surrounding rock is enhanced, the stability of the surrounding rock of the roadway is guaranteed, and the safe production of the mine is maintained. [ABSTRACT FROM AUTHOR]

Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively. [ABSTRACT FROM AUTHOR]

Recently, impacts of microRNAs have been unraveled in human diseases, and we aimed to confirm the role of miR‐30b/30d in fulminant hepatic failure (FHF). Expression of miR‐30b/30d and CEACAM1 in serum of FHF patients and healthy people was measured by reverse transcription quantitative polymerase chain reaction (RT‐qPCR) and Western blot analysis. Mice FHF models were established by injection of D‐Galn and lipopolysaccharide, and were treated with miR‐30b/30d mimics. Oxidative stress, liver injury, and inflammatory reaction in mouse liver tissues were measured using oxidative stress‐related factor kits, hematoxylin–eosin staining and enzyme‐linked immunosorbent assay, respectively. Moreover, cell cycle distribution and apoptosis of hepatocytes of mice were determined by flow cytometry, and the target relation between miR‐30b/30d and CEACAM1 was confirmed by bioinformatic method and dual luciferase reporter gene assay. MiR‐30b/30d expression was positively, and CEACAM1 expression was negatively related to prognosis of FHF patients. Up‐regulation of miR‐30b/30d attenuated oxidative stress, liver injury, and inflammatory reaction, and improved survival rate of FHF mice. Furthermore, elevated miR‐30b/30d ameliorated apoptosis and cell cycle arrest of hepatocytes of FHF mice. CEACAM1 was a target gene of miR‐30b/30d. This study highlights that up‐regulated miR‐30b/30d attenuates the progression of FHF by targeting CEACAM1, which may be helpful to FHF treatment. [ABSTRACT FROM AUTHOR]

Cemented paste backfill (CPB) is an eco-friendly composite containing mine waste or tailings and has been widely used as construction materials in underground stopes. In the field, the uniaxial compressive strength (UCS) of CPB is critical as it is closely related to the stability of stopes. Predicting the UCS of CPB using traditional mathematical models is far from being satisfactory due to the highly nonlinear relationships between the UCS and a large number of influencing variables. To solve this problem, this study uses a support vector machine (SVM) to predict the UCS of CPB. The hyperparameters of the SVM model are tuned using the beetle antennae search (BAS) algorithm; then, the model is called BSVM. The BSVM is then trained on a dataset collected from the experimental results. To explain the importance of each input variable on the UCS of CPB, the variable importance is obtained using a sensitivity study with the BSVM as the objective function. The results show that the proposed BSVM has high prediction accuracy on the test set with a high correlation coefficient (0.97) and low root-mean-square error (0.27 MPa). The proposed model can guide the design of CPB during mining. [ABSTRACT FROM AUTHOR]

Cemented paste backfill (CPB) is an eco-friendly composite containing mine waste or tailings and has been widely used as construction materials in underground stopes. In the field, the uniaxial compressive strength (UCS) of CPB is critical as it is closely related to the stability of stopes. Predicting the UCS of CPB using traditional mathematical models is far from being satisfactory due to the highly nonlinear relationships between the UCS and a large number of influencing variables. To solve this problem, this study uses a support vector machine (SVM) to predict the UCS of CPB. The hyperparameters of the SVM model are tuned using the beetle antennae search (BAS) algorithm; then, the model is called BSVM. The BSVM is then trained on a dataset collected from the experimental results. To explain the importance of each input variable on the UCS of CPB, the variable importance is obtained using a sensitivity study with the BSVM as the objective function. The results show that the proposed BSVM has high prediction accuracy on the test set with a high correlation coefficient (0.97) and low root-mean-square error (0.27 MPa). The proposed model can guide the design of CPB during mining. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) is a promising engineering material nowadays. This study carried out four-point-bending fracture experiment on wide-notch-concrete beams reinforced with near-surface mounted (NSM) CFRP rebar, aiming at exploring the effect of CFRP rebar on beam fracture characteristics. The test comprised four groups concrete beam specimens (three for each) with varying reinforcing methodologies. The first group specimens were common concrete beams strengthened by one near-surface mounted CFRP bar, while the second group consisted of common concrete beams strengthened by two near-surface mounted CFRP bars. On the issue of the third group, there were three wide-notch concrete beams strengthened with one near-surface mounted CFRP bar. Lastly, three wide-notch concrete beams strengthened with two near-surface mounted CFRP bars form the group four. The study proposed various equations about the rectangular concrete beams strengthened with near-surface mounted CFRP rebar under tension-shearing condition. For both open cracks and closed cracks, the equations concluded stress intensity factor as well as extending angle in mode I and II crack types. Four-point-bending fracture experiments for wide-notch-concrete beams and numerical simulation experiments were included in this research. This research exhibited expanding direction of wing type crack under effective shearing stress by the superposition model of crack tip stress intensity factor and stress intensity factor. Four-point-bending fracture tests for wide-notch-concrete beams and numerical simulation tests are finished in this research. The tests demonstrate characteristics of crack expanding direction, extending angle, vertical displacement, maximum normal stress, shear strain rate, distribution of plastic zones. Combining the theory and reality, the experimental results were in close agreement with the numerical research. [ABSTRACT FROM AUTHOR]

Self‐compacting concrete (SCC) is a cementitious composite which serves complex formworks without mechanical vibrations with superior deformability and high resistance to segregation. Besides, the recycled aggregate concrete (RAC) is also developing rapidly and along with the ever‐increasing sustainable demand for infrastructure. The combination of the fibers, RAC, and SCC may create advantages for the construction industry. In this study, the polypropylene (PP) fiber at 0.1, 0.15, 0.2, and 0.25% volume fractions and steel fibers at 0.25, 0.5, 0.75, and 1% volume fractions are introduced into fiber‐reinforced recycled aggregate self‐compacting concrete (FR‐RASCC). Both fresh property and hardened mechanical performance, comprising compressive and tensile strengths and modulus of elasticity are analyzed. The fibers validate the optimal 0.1% volume fraction for PP fiber and 0.75% volume fraction for steel fiber. In addition, the results are proved to enhance the mechanical properties and reduce cracking despite the negative impact on the fresh property. Moreover, the experimental outcomes are compared with previous researches to establish the linear model, demonstrating the relationship between fiber fraction and the mechanical properties. [ABSTRACT FROM AUTHOR]

Self‐compacting concrete (SCC) has developed rapidly in modern concrete structures to accommodate the need of high deformability and considerable resistance to segregation. The lightweight aggregate called scoria is also utilized to manufacture lightweight self‐compacting concrete (LWSCC) to improve the strength‐to‐weight ratio with considerable cost beneficial. Meanwhile, the LWSCC employs fibers (polypropylene [PP] and steel) equipping different replacement ratios of traditional aggregates, the SCC will be invented to reveal balanced fresh property, spalling behavior, mechanical performance especially at elevated temperatures. In this study, eight LWSCC mix designs were explored with varying fiber ratios by volume of PP fiber (0.1, 0.15, 0.20, and 0.25%) and steel fiber (0.25, 0.5, 0.75, and 1.0%). The effects of different fiber types and ratios are tested with fresh properties (slump flow and J‐ring), hardened mechanical performance (compressive and tensile strength at 20°C) and high temperature resistance (compressive and tensile strength, mass loss and spalling) after 28‐day curing at 100, 300, 600, and 900°C. The 0.25% optimum fiber ratio for PP fiber mix and 0.75% for steel fiber mix are determined with balanced fresh properties as well as high‐temperature resistance for the hardened properties. [ABSTRACT FROM AUTHOR]

Summary: This investigation studied the coalcrete, a new supporting material produced by jet grouting (JG) for supporting surrounding coal seams. For support design, the unconfined compressive strength (UCS) of the coalcrete is an essential parameter to evaluate the jet grouting effect in coal mines. In this study, an intelligent technique was proposed for predicting the UCS of the coalcrete by combining back propagation neural network (BPNN) and beetle antennae search (BAS). The architecture of BPNN was first tuned by BAS, and then, the optimized BPNN‐BAS model was subsequently used for nonlinear relationship modeling. Several crucial influencing variables including water‐cement ratio, coal‐grout ratio, and curing time were selected as the inputs. By combining these variables, 360 coalcrete samples were prepared in a controlled laboratory environment and tested for establishing the dataset. The results demonstrate that BAS can tune the BPNN architecture more efficiently compared with random selection. Moreover, in comparison with multiple regression (MLR) and logistic regression (LR), and support vector machine (SVM), the optimized BPNN‐BAS model is more reliable and accurate for predicting coalcrete strength. Sensitivity analysis (SA) was used to obtain the variable importance, and the results demonstrate that curing time affects the UCS of the coalcrete most strongly, followed by water‐cement ratio and coal‐grout ratio. The success of this study provides an accurate and brief approach to coalcrete strength prediction. [ABSTRACT FROM AUTHOR]

A concrete column has a significant influence on constructions because the component withstands the major weight of the whole structure. However, the ordinary concrete column has limited capacity to resist to multiaxial loadings. Since steel plate is one of the most common materials used in construction, concrete reinforced with steel plates based on prestressing reinforcement technology would be a directly applicable solution to the enhancement of mechanical property. In this study, a new reinforcement method upon concrete cylinder column used a two-way prestressed semicircular steel plate, which has already been patented. The study firstly analyzed the stress-bearing situation of the short concrete column reinforced by a two-way prestressed semicircular steel plate. The considerable substitution effect of vertical prestressing force on the concrete column was explored. The multiple restriction effects of horizontal prestressing force, semicircular steel plate, and stirrup were examined. Thereby, the calculation model was derived for the prestressed structure. Secondly, the formula was verified by experiments with close agreements. The result indicated that a unified strength theory was applicable to the theoretical calculation for short concrete column reinforced with a prestressed semicircular steel plate under axial compression, providing the foundation for analytical computation of prestressed semicircular steel plate-reinforced concrete column structures. [ABSTRACT FROM AUTHOR]

As large-scale wind farms (WFs) are integrated with the power grid, the interaction between the WF and the grid may excite torsional vibration of the shaft of the wind turbine. To study the shaft's torsional vibration characteristics of a doubly fed induction generator (DFIG)-based WF, a detailed small-signal model of DFIG is established first. Then the small-signal model for a WF composed of multiple DFIGs is developed on the basis of the single-machine model. Modal analysis is employed to investigate the torsional vibration characteristics of a WF made up of several identical DFIGs, whose accuracy is also demonstrated through time-domain simulation. To simplify the torsional vibration analysis for the DFIG-based WF, a reduced-order equivalent model is proposed. The results obtained from modal analysis show that the equivalent model not only precisely maintains all the torsional vibration modes of the original small-signal model but also greatly reduces the computation complexity. With the equivalent model, the 'curse of dimensionality' problem is solved in torsional vibration analysis for large WFs, which is of great help in the design of further damping schemes. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

This study investigated the use of a newly developed chitosan-Ca pectinate microbead formulation for the colon-targeted delivery of anti-A/B toxin immunoglobulin of egg yolk (IgY) to inhibit toxin binding to colon mucosa cells. The effect of the three components (pectinate, calcium chloride, and chitosan) used for the microbead production was examined with the aim of identifying the optimal levels to improve drug encapsulation efficiency, swelling ratio, and cumulative IgY release rate. The optimized IgY-loaded bead component was pectin 5% ( w/ v), CaCl 3% ( w/ v), and chitosan 0.5% ( w/ v). Formulated beads were spherical with 1.2-mm diameter, and the drug loading was 45%. An in vitro release study revealed that chitosan-Ca pectinate microbeads inhibited IgY release in the upper gastrointestinal tract and significantly improved the site-specific release of IgY in the colon. An in vivo rat study demonstrated that 72.6% of biologically active IgY was released specifically in the colon. These results demonstrated that anti-A/B toxin IgY-loaded chitosan-Ca pectinate oral microbeads improved IgY release behavior in vivo, which could be used as an effective oral delivery platform for the biological treatment of Clostridium difficile infection (CDI). [ABSTRACT FROM AUTHOR]

High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN's hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented. [ABSTRACT FROM AUTHOR]

Sensing technologies demonstrate promising potential in providing the construction industry with a safe, productive, and high-quality process. The majority of sensing technologies in the construction research area have been focused on construction automation research in prefabrication, on-site operation, and logistics. However, most of these technologies are either not implemented in real construction projects or are at the very early stages in practice. The corresponding applications are far behind, even in extensively researched aspects such as Radio Frequency Identification, ultra-wideband technology, and Fiber Optic Sensing technology. This review systematically investigates the current status of sensing technologies in construction from 187 articles and explores the reasons responsible for their slow adoption from 69 articles. First, this paper identifies common sensing technologies and investigates their implementation extent. Second, contributions and limitations of sensing technologies are elaborated to understand the current status. Third, key factors influencing the adoption of sensing technologies are extracted from construction stakeholders' experience. Demand towards sensing technologies, benefits and suitability of them, and barriers to their adoption are reviewed. Lastly, the governance framework is determined as the research tendency facilitating sensing technologies adoption. This paper provides a theoretical basis for the governance framework development. It will promote the sensing technologies adoption and improve construction performance including safety, productivity, and quality. [ABSTRACT FROM AUTHOR]

Pre-grouting as an effective means for improving the stability of roadways can reduce maintenance costs and maintain safety in complex mining conditions. In the Guobei coal mine in China, a cement pre-grouting technique was adopted to enhance the overall strength of soft coal mass and provide sufficient support for the roadway. However, there are very limited studies about the effect of grouting on the overall strength of coal in the laboratory. In this paper, based on the field observation of a coal-grout structure after grouting, a series of direct shear tests were conducted on coal and grouted coal specimens to quantitatively evaluate the quality improvement of grouted coal mass. The results showed that the peak and residual shear strength, cohesion, friction angle and the shear stiffness of grouted coal were significantly improved with the increase of the diameter of grout column. Linear regression models were established for predicting these mechanical parameters. In addition, three failure models associated with coal and grouted coal specimens were revealed. According to microstructure and macroscopic failure performance of specimens, the application of the proposed models and some methods for further improving the stability of grouted coal mass were suggested. The research can provide the basic evaluation and guideline for the parametric design of cement pre-grouting applications in soft coal mass. [ABSTRACT FROM AUTHOR]

The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD's automatic design solutions, while improving BIM's capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers' future studies. [ABSTRACT FROM AUTHOR]