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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]

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]

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]

CVN (cyanovirin-N) is an anti-HIV protein. CVNH (cyanovirin-N homology) represents its homology. In a previous study, we first reported the full-length sequences of the CVNH gene cloned from Ceratopteris thalictroides. Based on the finding, the coding sequence of CtCVNH was optimized in the study, and then a pET prokaryotic expression vector was constructed. The purification and identification of CtCVNH protein were investigated, as well. SDS-PAGE analysis indicated that a 31 kDa protein was overexpressed and mainly accumulated in the soluble fraction. Only a single protein was obtained after the Ni- nitrilotriacetic acid (NTA) affinity chromatography. The purified protein was identified to be the recombinant CtCVNH by both Western blot and peptide mass fingerprinting analysis.