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To promote the construction industry and establish a green environment, the integration of sustainable building materials is urgently required. This study presents the influences of three materials (silica fume, fly ash, and recycled ceramic powder (RCP)) and different RCP dosages (0%, 10%, 20%, and 30%) on the mechanical properties and microstructure of concrete at different temperatures. The study presents the results of comprehensive flow tests, printability assessments, mechanical evaluations under different loading directions, high-temperature analyses, digital image correlation, X-ray powder diffraction and scanning electron microscopy. The experimental findings indicate the successful printing of all samples, with wall structure failure occurring only at a height of 730 mm. Furthermore, compared to those of other materials, the RCP displays a superior heat resistance, with a significant increase in compressive strength (28.9%) to 53.9 MPa at 300 °C. Additionally, the introduction of the RCP into 3D-printed concrete results in an anisotropic behaviour, with an initial enhancement in the mechanical strength, followed by attenuation as the RCP content increases. Microstructural analysis reveals enhanced interfacial adhesion with RCP addition, albeit accompanied by the emergence of numerous pores. The optimal mixture, which is designated as RCP-2-0-1 with 20% of the cement replaced by RCP, exhibits a significant anisotropic compressive strength (55.9 MPa) and an adequate anisotropic flexural strength (12.49 MPa) and impressive heat resistance (11.7% increase in compressive strength at 300 °C). This renders it a suitable sustainable impact-resistant thermal material for use in structural applications.

The increasing concern over electromagnetic wave (EMW) contamination has led to the emergence of the EMW-absorbing superstructure element. Cementitious composites typically possess poor electromagnetic wave reflectivity, but this was improved through 3D printing technology, which offers greater productivity and design flexibility. In this study, a new wave-shaped EMW absorbing superstructure was manufactured using 3D cement printing, and mechanical testing showed a significant improvement in flexural and shearing strength. Molecular dynamic simulation was utilized to investigate the mechanism of the enhanced mechanical properties at the molecular scale. The EMW absorption performance of the printed specimens was analyzed from 1 GHz to 18 GHz through the Naval Research Laboratory (NRL) equipment, exhibiting a significant improvement in reflectance, especially in the low-frequency domain. Based on experimental results, the optimized design of the superstructure was proposed, which possesses an average reflection loss of −25 dB, a peak reflectivity of −37.4 dB, and an absorbing bandwidth of 17 GHz. These findings suggest that the 3D-printed EMW absorber element has great potential for use in minimizing electromagnetic contamination in various applications.

To explore the feasibility of using ultra-high performance concrete (UHPC) to strengthen reinforced concrete (RC) beams in torsion, pure torsion tests on 9 specimens have been conducted in this study, including one RC beam without strengthening (referred as control beam) and 8 UHPC-strengthened beams. The variables considered for investigation included the wrapping form of UHPC layer, thickness of UHPC layer, shape of steel fibers added in UHPC layer, UHPC-RC interfacial treatment, and reinforcement ratio of UHPC layer. The overall torsional behavior, torque–twist curves, torque-principal strain curves, cracking pattern and UHPC-RC interfacial slippage for all specimens were obtained based on the torsion tests. Results show that the cracking and ultimate torsional moments of all strengthened beams were higher than those of the control beam, with maximum increases of 488.5% and 593.2%, respectively. When the RC beam cannot be fully wrapped, which is the preferred strengthening scheme, 3-sided wrapping form is also an acceptable choice, but the 2-sided strengthening scheme should be avoided. The determination of the thickness of UHPC layer should comprehensively consider the strengthening effect, cost for strengthening and requirements for RC beam's dimensions after strengthening. RC beam surfaces should be fully roughened before casting UHPC to ensure that the UHPC layers and the RC beam persistently act together as a whole to resist torsional loads. The UHPC layers are recommended to be equipped with steel bars, which could substantially improve the torsional capacity and ductility of the strengthened beam on the premise of limited increase in the cost. Finally, the effectiveness of torsional strengthening using UHPC was compared with that using fiber reinforced polymer (FRP) in terms of increase in cross-sectional area, increases in cracking and ultimate torsional moments, increase in torsional stiffness, failure mode and long-term performance.

This work presents the design and application of a low-cycle reciprocating loading test on 23 recycled aggregate concrete-filled steel tube columns and 3 ordinary concrete-filled steel tube columns. Additionally, a systematic study on the influence of various parameters (e.g., slenderness ratio, axial compression ratio, etc.) was conducted on the seismic performance of the specimens. The results show that all the specimens have good hysteresis performance and a similar development trend of skeleton curve. The influence of slenderness ratio on the seismic index of the specimens is more significant than that of the axial compression ratio and the steel pipe wall thickness. Furthermore, artificial intelligence was applied to estimate the influence of parameter variation on the seismic performance of concrete columns. Specifically, Random Forest with hyperparameters tuned by Firefly Algorithm was chosen. The high correlation coefficients (R) and low root mean square error values from the prediction results showed acceptable accuracy. In addition, sensitivity analysis was applied to rank the influence of the aforementioned input variables on the seismic performance of the specimens. The research results can provide experimental reference for the application of steel tube recycled concrete in earthquake areas.

Conductive fillers, such as graphite particles (GP), steel slags (SS), and ground granulated blast furnace slag (GGBS) have been widely utilized in designing electrically conductive cementitious composites (ECCC) for various applications, including traffic detection, structural health monitoring (SHM), and pavement deicing. Owing to the complex working field, a comprehensive understanding of the role that the conductive fillers played in ECCC is essential for designing high-performance ECCC. In the present study, mechanical and conductivity experiments were conducted to explore the influences of these fillers on ECCC performances in strengths and electrical resistance. In addition, the reactive molecular dynamic (MD) simulation was firstly performed to quantify the interfacial properties of GP, SS, and GGBS in ECCC at the molecular level. Simulation results indicated that the chemical components of these conductive fillers dominate the atomic interfacial properties. Mineral components in SS or GGBS, especially Al2O3and SiO2, led to a stronger interfacial bonding with cement in comparison to graphite in GP. At last, a hybrid mixing design of GP and SS was proposed in this study, balancing the mechanical and conductive performance of ECCC.

Self-compacting concrete (SCC) mixtures requires a large amount of Portland cement and thus resulting in high CO2emissions, expanding its application has been in question to environment sustainability. Accordingly, environmental-friendly alternatives to Portland cement in SCC mixtures are deemed necessary for the progress of SCC in construction. In this investigation, treated jute fibers and mineral powders were used to replace cement in SCC mixtures. The effects of fibers and powder contents, fiber treatment, and mineral powder types on the workability and mechanical properties of SCC mixtures were investigated through slump-flow, compressive, and flexural tests. A microstructure was conducted through scanning electron microscopy. It was concluded that mineral powders and jute fibers could significantly improve the workability and mechanical properties of SCC. The microstructure observations demonstrated that the proposed methods are capable of enhancing the mechanical properties of SCC with an optimum fiber content. Specifically, a combination of 0.1% jute fibers and 75 μm zeolite powders were found to achieve the best mechanical properties. The relevant results supply fundamental reference and background for artificial intelligence and multi-function development for future infrastructure.

The glass fiber-reinforced polymer (GFRP) rebar reinforced cemented soil is widely employed to solve the weak foundation problem led by sludge particularly. The robustness of this structure is highly dependent on the interface bond strength between the GFRP tendon and cemented soils. However, its application is obstructed owing to the deficient studies on the influence factors. Therefore, this study investigates the effects of water content (Cw: 50%–90%), cement proportion (Cc: 6%–30%), and curing period (Tc: 28–90 days) on peak and residual interface bond strengths (Tpand Tt), as well as the unconfined compression strength (UCS). Results indicated that mechanical properties were positively responded to Tcand Cc, while negatively correlated to Cw. Besides, Random Forest (RF), one of the machine learning (ML) models, was developed with its hyperparameters tuned by the firefly algorithm (FA) based on the experimental dataset. The pullout strength was predicted by the ML model for the first time. High correlation coefficients and low root-mean-square errors verified the accuracy of established RF-FA models in this study. Subsequently, a coFA-based multi-objective optimisation firefly algorithm (MOFA) was introduced to optimise tri-objectives between UCS, Tp(or Tt), and cost. The Pareto fronts were successfully acquired for optimal mixture designs, which contributes to the application of GFRP tendon reinforced cemented soil in practice. In addition, the sensitivity of input variables was evaluated and ranked.

Waste glass is inert and non-degradable which leads to enormous environmental and sustainability troubles, but it can be reused in concrete due to the potential of the pozzolanic activity. This study proposes methods on activity excitation of waste glass powder (WGP) including mechanical, chemical, and mechanical-chemical activation. The results showed that the mortar containing 30% 75 μm WGP activated by the mechanical-chemical method was optimal to increase the mechanical property and reduce the detrimental expansion. In addition, the microstructural analysis was conducted to explore the activation effect on WGP and WGP-cement system. An artificial intelligence (AI) based multi-objective optimisation (MOO) model was proposed to seek the optimal mix proportions for the unconfined compression strength (UCS), alkali-silica reaction (ASR), and cost. A comprehensive dataset was investigated including 549 specimens for the UCS test and 366 test results for the expansion test. Random Forest (RF) model was utilized for the prediction of UCS and ASR values with hyperparameters tuned by a firefly algorithm (FA). The high correlation coefficients (0.93 for UCS and 0.91 for ASR) verified the feasibility of FA-RF. Subsequently, the FA-RF model was extended as the objective function for the mi-objective firefly algorithm (MOFA-RF) to obtain the consequent Pareto fronts. This paper combined the results of experiments, machine learning prediction, and multi-objective optimisation design for activated WGP mortar, which provided a comprehensive basis for the practical application.

Shear design of RC beams with and without stirrups using laboratory experiments is difficult or even impossible as a large number of variables need to be considered simultaneously, such as the span-to-depth ratio, web width and reinforcement ratio. In addition, due to the complex shear failure mechanism, empirical approaches for shear design are derived within the boundaries of their own testing regimes. Thus, the generalization ability and applicability of these approaches are limited. To address this issue, this study uses machine learning approaches for shear design. A random forest model is constructed to predict the shear strength of RC beams. The hyperparameters of RF are tuned using beetle antennae search algorithm modified by Levy flight and inertia weight. The developed model is trained on two data sets of RC beams with and without stirrups containing 194 and 1849 samples, respectively. The obtained model has high prediction accuracy with correlation coefficients of 0.9367 and 0.9424 on these two test data sets, respectively. The proposed method is powerful and efficient in shear design of RC beams with and without stirrups and therefore paves the way to intelligent construction.

Waste glass is promising to be recycled and reused in construction for sustainability. Silicon dioxide is the main component of glass, however, its pozzolanic activity is latent mainly due to its stable silica tetrahedron structure. To excite the activation of waste glass, chemical activation and mechanical grinding of waste glass powder (WGP) were investigated. As the supplementary, hydrothermal and combined (mechanical-chemical-hydrothermal) treatments were conducted on part of the WGP samples. The unconfined compression strength (UCS), expansion caused by alkali–silica reaction (ASR), and the microstructural morphology of WGP were investigated. The results showed the dosage threshold (around 2%) of the chemical activators (alkali and sodium sulfate) and the combined activation were optimal. Besides, a firefly algorithm (FA) based multi-objective optimisation model (MOFA) was applied to seek the Pareto fronts based on three objectives: UCS, ASR expansion, and Cost of mixture proportion. The objective functions of UCS and expansion were established through training the machine learning (ML) models where FA was used to tune the hyperparameters. The cost was calculated by a polynomial function. The ultimate values of root mean square error (RMSE) and correlation coefficient (R) showed the robustness of the ML models. Moreover, the Pareto fronts for mortars containing 300 μm and 75 μm WGPs were successfully obtained, which contributed to the practical application of waste glass in mortar production. In addition, the sensitivity analysis was conducted to rank the importance of input variables. The results showed that curing time, activator's content, and WGP particle size were three essential parameters.

Eco-friendly concrete becomes research hotspots since concrete production emits 8-10% of the total anthropogenic CO2emissions worldwide. Industry solid waste modification exhibits great potentials on mitigating aggregates depletion and carbon emissions. Coal gangue aggregate (CGA) coated by silica fume manually (CGACM) and in a desiccator (CGACD) are utilised to optimize the 3D printing concrete. Specimens were printed by a 3D printing robotic arm with a 25mm-diameter nozzle. The compressive strength of CGACM and CGACD increases by 49% and 44% than non-activated series. For splitting tensile strength, the figures are 43% and 36%. The density of activation series decreases over 16% compared with standard sand. However, both surface pretreated methods make negative effects on structure compactness. From the SEM, SiO2particles filles defects of aggregates, resulting in a denser internal structure and promoting secondary hydration reaction. However, excessive SiO2particles agglomerated on CGA leads to the lubricant film among particles. These results expand recycle methods for CG and promotes 3D printing technology application on mine.

Industrial solid wastes, such as ultra-fine fly ash (RUFA) and ground granulated blast-furnace slag (GGBS), hold tremendous potential for recycling due to their abundance and excellent pozzolanic properties. However, red mud (RM), a highly alkaline waste, has not received comprehensive attention in the construction industry. Thus, this study investigates the synergistic effect and geopolymerization mechanism of industrial solid wastes in ternary cementless composites based on RM, RUFA, and GGBS. The experimental program includes macroscopic comparative analyses of water consumption, fluidity, setting time, permeability, and compressive strength of the ternary cementless composite. Additionally, SEM and EDS analyses examine the microscopic morphology and elemental distribution of the composite. The results indicate that RM prolongs the setting time while reducing fluidity and compressive strength. A 30% increase in RM content led to a substantial rise in the initial and final setting time by approximately 191.6% and 164.7%, respectively. In contrast, GGBS enhances the compressive characteristics of the composite systems but shortens the setting time and reduces fluidity. Notably, Group D4 demonstrates the highest compressive strength, reaching an impressive 47.3 MPa. Moreover, the content of GGBS influences the hydration products in the ternary cementless composite. At low GGBS content, the material contains a significant amount of N-A-S-H gels, whereas at high GGBS content, partial N-A-S-H gels are replaced by C-A-S-H gels. These findings significantly enhance our understanding of the synergistic effect and geopolymerization mechanism of RM, RUFA, and GGBS, thereby facilitating the reuse of these three industrial solid wastes.

For low-carbon sustainability, recycled rubber particles (RPs) and recycled aggregate (RA) could be used to make rubber-modified recycled aggregate concrete (RRAC). The characteristics (compressive strength and peak strain) of RRAC with various amounts of RA and RPs after heating at various temperatures were studied in this work. The results show that high temperatures significantly decreased the uniaxial compressive strength (UCS), whereas the addition of RA (e.g., 50%) and RPs (e.g., 5%) can mitigate the negative effect caused by high temperatures. The peak strain can also be improved by increasing the replacement ratios of RA and RP. Support vector regression (SVR) models were trained using a total of 120 groups of UCS and peak strain experimental datasets, and an SVR-based multi-objective optimization model was proposed. The excellent correlation coefficients (0.9772 for UCS and 0.9412 for peak strain) found to illustrate the remarkable accuracy of the SVR models. The Pareto fronts of a tri-objective mixture optimization design (UCS, strain, and cost) were successfully generated as the decision reference at varying temperature conditions. A sensitivity analysis was performed to rank the importance of the input variables where temperature was found as the most important one. In addition, the replacement ratio of RA is more important compared with that of the RP for both the UCS and strain datasets. Among the mechanical properties of concrete, compressive strength and peak strain are two key properties. This study provides guidance for the study of RRAC constitutive models under high temperatures.

To promote the sustainable development of the construction industry, this study examined the influence of the rubber particles addition on the fresh and hardened engineering properties of rubber particle (RP) modified 3D printable sustainable cementitious composites (3DPSCC) incorporating polypropylene (PP) fibers, in which RP derived from waste tires partially replaced sand between 0% and 100% volume replacement. A series of flowability tests, printing tests and mechanical tests in different loading directions were carried out to investigate the printability and anisotropic mechanical properties of the 3DPSCC. The results show that all test specimens can be successfully printed due to their good workability, extrudability, and buildability in the fresh state. The compressive, flexural strength, and impact strengths of 3DPSCC, in general, decreased with the increasing RP content and exhibited obvious anisotropy, which could be owing to the presence of both inter-layer and intra-layer interfaces in each group of specimens during the printing process. The impact resistant of the modified specimens improved with the addition of RP, as the average maximum displacement gradually increased from 0.41mm to 0.92mm, despite the weakening strength. The optimal mixture was regarded as RP-40 (40% RP replacement) exhibiting comparable anisotropic compressive strength (higher than 30MPa), enough anisotropic flexural strength (higher than 4MPa), and superior energy dissipation capacity (highest among all 3DPSCC), which can meet the strength requirement of concrete (i.e., 25MPa for steel reinforced concrete structure) and is suitable for structural applications as a sustainable impact-resistant material.

Abstract: The problem of the hard roof hanging in deep mines may cause large deformations of roadways, which even jeopardizes the safety of personnel. Finding an effective and safe method to reduce the high stress caused by that is of significance. In this study, the stress relief technique was employed to 6305 working face in the Dongtan coal mine to maintain the roadway stability. The theoretical analysis, numerical modeling, and field practice were utilized to investigate the effect of the stress relief technique on the main hard roof. The range of pre-splitting cutting angles by stress relief technique was first analyzed theoretically. Furthermore, the stress evolution of the surrounding rock along the goaf, the best breaking position, and patterns of the main roof were obtained by numerical simulation. Also, the field test was carried out and the results showed that the total deformation of roadway is controlled within 30–60 mm, and the change rate of anchor cable stress is controlled within the range of 13 to 16%, ensuring the long-term stability of the roadway along the goaf. Successful practical engineering provides a guideline in similar conditions especially for the stability of deep underground space under high-stress environments. Article highlights:

A systematical study of the stress relief technique in deep mines from theoretical analysis to field application.

The best cutting angles along hard roof goaf is 75° based on the universal distinct element simulation.

The designed parameters achieved less roadway deformation by the field monitoring.

Abstract: An abstraction method developed for the explicit linear temporal logic model checking was geared towards reducing the useless part of the state space during the abstraction period. This reduces the cost during the abstraction period relative to models requiring many useless states. A dining-philosophers example comparing this abstraction method with conventional methods indicates that a large proportion of the state space has been reduced by this abstraction method. Finally, the abstract method is shown to be correct and an analysis is given to show how such a large proportion of states can be reduced. [Copyright &y& Elsevier]