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Electromagnetic wave (EMW) pollution poses a tremendous effect on information security and human health. However, ordinary concrete structure incorporated by ferrite fiber lacks EMW absorption flexibility to form electromagnetic superstructures. 3D printing technology paves an effective way to facilitate the anisotropy of electromagnetic absorption capacity by generating directional effects on ferrite fiber. This research evaluates the influence of 3D-printed fiber-oriented superstructure on EMW absorption performance with an equivalent waveguide attenuator model. The microwave-absorbing cementitious composite (10% magnetite and 25% copper slag) was prepared to incorporate 0.5 wt% copper fibers (CF) and steel fibers (SF), respectively. Absorption elements in each group are prepared by laminar parallel printing, cross-printing, and zigzag printing. In addition, the EMW absorption capability (ranging from 2 GHz to 18 GHz) was investigated by the Naval Research Laboratory (NRL) equipment. The overall EMW absorption performance of the SF samples is superior to CF samples. The optimized order of the EMW absorption performance of CF-reinforced samples is determined as zigzag, parallel, and cross printing, while SF is parallel, cross, and zigzag printing methodology. Overall, the laminar parallel printed steel fiber element gave the best shot with a peak reflectivity of − 16.34 dB and an absorption bandwidth of 13.15 GHz. Meanwhile, SF-reinforced specimens all demonstrated absorption peaks around 8 GHz, while CF-incorporated samples' absorption peaks appeared at both 8 GHz and 12 GHz, offering multiple design and application choices according to engineering requirements. Finally, an equivalent attenuator model is suggested for illustrating the superimposed reinforcement, spatial impedance matching, and multiple scattering of dielectric properties. • The prominent fiber orientation effect induced by 3D printing facilitates the enhancement of EMW anisotropic properties. • Copper fibers with slender dimensions form abundant and flexible multi-size equivalent waveguide attenuator structures that promote vector superposition and interference effects, exhibiting RL peaks at both 7 GHz and 12 GHz. • The steel fiber-reinforced samples are superior to the fine copper fiber samples in terms of overall electromagnetic absorption performance. The steel fibers and the surrounding concrete exhibit various gradients in their ability to adsorb EMW, resulting in a completely absorbing superstructure. • The electromagnetic wave absorption mechanism of the equivalent waveguide attenuator model for the fiber-reinforced composite is analyzed in detail. [ABSTRACT FROM AUTHOR]

• A novel method was proposed for predicting permeability and unconfined compressive strength of pervious concrete. • 270 samples were prepared for building the dataset. • Permeable and mechanical properties of pervious concrete were elucidated. • Beetle antennae search was firstly used to tune the hyper-parameters of support vector regression. • The support vector regression model tuned by beetle antennae search algorithm has high prediction accuracy. Pervious concrete is a widely used construction material thanks to its good drainage characteristics. Before application, its most important properties, i.e. the permeability coefficient (PC) and 28-day unconfined compressive strength (UCS) are required to be tested. However, conducting PC and UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, an evolved support vector regression (ESVR) tuned by beetle antennae search (BAS) to accurately and effectively predict the PC and UCS of pervious concrete. To prepare the dataset of the ESVR model, 270 specimens in total were prepared and casted in a controlled environment in the laboratory. The water-to-cement (w/c) ratio, aggregate-to-cement (a/c) ratio, and aggregate size were selected as the crucial influencing variables for the inputs, while PC and UCS were the outputs of this model. The results indicate that both the PC and UCS firstly increased and then decreased with increasing w/c ratio. As the a/c ratio increased, PC increased, while UCS decreased. Moreover, BAS is more reliable and efficient than random hyper-parameter selection for hyper-parameter tuning. A low root-mean-square error (RMSE) and high correlation coefficient (R) indicate a relatively high predictive capability of the proposed ESVR model. The sensitivity analysis (SA) suggests the a/c ratio and aggregate size were the most sensitive variables for UCS and PC, respectively. This pioneering work provides a simple and convenient method for evaluating PC and UCS of pervious concrete. [ABSTRACT FROM AUTHOR]

• Nano-graphite as a functional filler incorporated into concrete can significantly improve its electrical conductivity. Meanwhile, the proper amount of nano-graphite addition can also increase its mechanical strength. • The addition of copper slag improved the mechanical properties and electrical conductivity of ECCC. • The chemical activation improved the reactivity of the volcanic ash in the fly ash–cement system. The ultrasonic vibration process facilitates the uniform distribution of nano-graphite. • Combined activation is the best method to improve mechanical properties and electrical conductivity, possessing the advantages of chemical and ultrasonic activation. Electrically conductive cementitious composite (ECCC) features structural material functions, electrical conductivity, and piezoresistivity properties broadly applied in snow melting, electromagnetic shielding, cathodic protection system, and structural health monitoring (SHM). Nano-graphite is an ideal ECCC functional filler since its ability to fill molecular pores, reduce concrete shrinkage and significantly improve their electrical conductivity. However, nano-graphite is high-cost and its excessive amounts can lead to particle agglomeration. Therefore, copper slag (CS) can partially replace NG to beneficially reuse the waste by-products and save energy for protecting the environment. Nevertheless, a single blend of fillers hardly exploits the potential mechanical and conductive properties. Consequently, different activation methods were adopted to obtain desirable dispersion and performances. This paper explored the influences of chemical alkali excitation, ultrasonic vibration, and combined activation on copper slag and nano-graphite wrapped ECCC. Experimental results from a total of 387 ECCC specimens with 16 design ratios demonstrated that the combined treatment of alkali excitation and ultrasonic vibration was superior to any single treatment. The optimal samples based on 3 wt% ratio of NG and 60 wt% of copper slag activated with combined treatment exhibited 44.55 MPa compressive strength, 6.65 MPa flexural strength, and 8180 Ω·cm electrical resistance. Lastly, an SEM was conducted to analyze the microstructure of the mixture and ECCC and a schematic diagram was proposed. [ABSTRACT FROM AUTHOR]

• WGP mortar activated by mechanical, chemical, hydrothermal, and combined treatment approaches. • UCS, flexural strength, ASR expansion, and microstructure were investigated. • The mechanical activation significantly enhanced the mechanical performance. • The chemical activation enhanced strength at early ages but negates ASR expansion. • The hydrothermal activation improved strength and is the best method to diminish the ASR expansion. • The combined activation was the optimal method to increase mechanical performance. Land disposal of waste glass results in hazardous environmental contamination. Such waste material should be reclaimed because it is inert and consequently nondegradable. The main component of glass is silica showing pozzolanic properties in cementitious matrices. Thus, the use of waste glass as a supplementary cementitious material in concrete is a sustainable solution to the land disposal of such waste materials. However, concrete incorporating waste glass suffers from deleterious expansion arising from alkali-silica reaction (ASR). This paper shows the effects of different activation approaches, including mechanical, chemical, hydrothermal, and combined activation, used to mitigate ASR. To this aim, activated waste glass powder (WGP) was produced using the aforementioned approaches and used in concrete as sand replacement at percentages of 0%, 10%, 20%, and 30%. The water to cement ratio remained unchanged for all mixes. Results showed that the combined activation was the optimal approach to increase mechanical property. The hydrothermal activation effectively reduced the detrimental ASR expansion, while the chemical treatment induced excessive expansion even for mixtures with a low WGP content of 10%. Also, microstructural analyses showed erosion on the surface of WGP activated by chemical and combined activation methods. Besides, few CH crystals were observed on 75 μm WGP mortar samples, illustrating the excellent pozzolanic activity on finer WGP. [ABSTRACT FROM AUTHOR]

• EMW-absorbing composite containing 6 wt% copper powder and 25 wt% copper slag. • EMW superstructures using 3D-printed oriented copper fiber upon microwave reflectivity. • Equivalent waveguide attenuator model and mechanism is proposed. • The copper fiber-reinforced element through crossed printing is the optimized configuration. • The superstructure creates a −20.43 dB reflectivity peak with an 11.79 GHz bandwidth. Electromagnetic wave (EMW) pollution negatively impacts information, equipment security, and the human body. However, neither powder phase absorbent nor ferrite fiber using traditional casting methodology cannot form electromagnetic superstructure to flexibly enhance EMW absorbing capacity. Thereby, ferrite fiber absorbent in well-oriented manner by 3D printing can be a feasible solution in EMW absorption due to its structural EMW anisotropy property. In this study, the influence of EMW superstructures using 3D-printed oriented copper fiber upon microwave reflectivity is investigated, and an equivalent waveguide attenuator model is proposed. First, an EMW-absorbing composite containing 6 wt% copper powder and 25 wt% copper slag is prepared. In copper slag cementitious composite, copper fiber (CF) and steel fiber (SF) are incorporated at 0.5 wt% separately. In each group, EMW-absorbing elements are manufactured through casting, laminar parallel printing, and cross printing. The superstructure anisotropy and EMW absorption performance are investigated through a network analyzer. The cross superstructure is outstanding in low-frequency absorption, while the parallel superstructure demonstrates desirable reflectivity at a high-frequency range. The copper fiber-reinforced element produced through crossed printing is determined as the optimized configuration, reaching a −20.43 dB reflectivity peak with an 11.79 GHz bandwidth. Lastly, an equivalent waveguide attenuator model is proposed to explain the superposition enhancement of the dielectric property, space impedance matching, and multiple scattering. [ABSTRACT FROM AUTHOR]

A multitude of antimony tailings (AT) solid wastes haphazardly accumulated in the open environment resulted in significant land resource occupation and serious ecological environment damage. AT as the fine aggregate additive in concrete is an effective approach for achieving solid waste resource utilization. Currently, 3D-printed AT concrete offers notable benefits in design flexibility, precision, material efficiency, productivity, and sustainability compared to conventional concrete. However, traditional AT-concrete is plagued by drawbacks including uneven strength distribution, protracted construction periods, and considerable resource consumption. Furthermore, the low reactivity and non-degradability of AT necessitate implementing modified activation techniques in AT concrete. This paper employs four modification methods (physical grinding, alkali excitation, hydrothermal activation, and combined activation) to activate AT. The modified AT is incorporated into concrete at five ratios (0 %,10 %, 20 %, 30 %, and 40 %) and integrated into a 3D printing system, manufacturing a total of 450 samples for mechanical property experiments. The result demonstrates that the combined modification method emerges as the optimal choice for enhancing the unconfined compressive strength (65.7 MPa) and flexural strength (12.9 MPa) in 3D printing. The SEM results revealed that the combined modification, alkali excitation, and hydrothermal activation led to AT depolymerization and sediment adsorption. Abundant C-S-H gel and a handful of Ca(OH) 2 crystals were observed on the physically modified AT (30 μm) surface, indicating sufficient pozzolanic reaction on 3D printed fine AT. • The 3D-printed method generally illustrates excellent impacts in strength enhancement. • AT physical modification achieves a substantial 20 % increase in mechanical properties. • Alkali-excitation modification notably increased the strength of low-content AT mortars by 35 %. • Hydrothermal modification boosts mix properties by 28% within a replacement ratio of 0 % to 30 %. [ABSTRACT FROM AUTHOR]

• A new program evaluating the impact of graphite and slag, and curing time. • Optimized slag and graphite to achieve balanced electrical and mechanical properties. • A microstructural interpretation to reveal distributions of conductive filler and C-S-H gel. Electrically conductive cementitious composites (ECCCs) have become a significant research interest in structural health monitoring. The use of graphite in ECCCs can significantly improve their electrical performance, however, with unsatisfactory friction resistance because of the graphite's smooth micro-surfaces. Slag can be incorporated with graphites into ECCCs to achieve good performance in both of mechanical resistance and electrical conductivity. This study investigated the impact of graphite and slag on the electrical and mechanical behaviors of ECCCs. Two hundred and eighty ECCC specimens were prepared with two different types of slags and with various conductivity ingredient fractions and curing times. The specimens were tested for compressive strength, flexural strength, and electrical resistance. It was concluded the 4% graphite content in ECCCs can significantly enhance electrical conductivity with moderate decrease in compressive and flexural strengths. Slags were found to improve both electrical conductivity and mechanical properties of ECCCs. The best results could be obtained with optimized contents of steel slag, blast furnace slag, and graphite. Finally, the microstructural mechanisms of the ECCC specimens were analyzed using scanning electron microscope (SEM) for graphite and slag. Variable sensitivity analysis was performed to allow for optimization of ingredient contents. [ABSTRACT FROM AUTHOR]

• The industrial by-products, solid wastes, carbon materials are discussed. • The steel fiber and ferrite exhibit satisfied EMW absorbing mechanical resistance. • The resonance materials improve EMW absorbing capacity with inferior strength. • The multi-phase and multi-layer composites and macrostructure are prospective. • The combination of 3D printing and EMW absorbing brings advantages. The ever increasing electromagnetic energy and multiple reflections of electromagnetic wave (EMW) impair human health and information security. The EMW absorbing cementitious material paves an innovative way to improve EMW absorbing performance of concrete structures. This study will elaborate on the EMW absorbing mechanism including energy exhaustion and impedance matching. A comprehensive review on the raw absorbent cementitious materials and the relevant preparation methods to be integrated into EMW absorbing concrete. Two new theoretical models are proposed including optimization zone model and equivalent electromagnetic parameter model to improve the current transform line model. Subsequently, the measurement and evaluation methodologies for EMW absorbing performance of cementitious materials are summarized. The expected research topics and applications of EMW absorbing cementitious materials are suggested. Through this critical review on the research status of EMW absorbing concrete, suggestions for optimizing the performance and workability of cementitious material are made to promote large-scale 3D printing technology. [ABSTRACT FROM AUTHOR]

• Four nano-sized graded absorbents were explored. • The 5 wt% coupling agent and 10 wt% dispersant was validated for graded absorbent. • The double-layer structure was made using spraying and extruding methodology. • The EMW mechanism for the graded double-layer structure was firstly summarized. • Theoretical calculation optimized a 15 mm absorbing layer and 15 mm impedance layer. The ever-increasing electromagnetic wave (EMW) energy and its reflections may impair human health and information security. An ordinary EMW-absorbing structure usually exhibits limited EMW-reflecting capacity given its ungraded structure in both the micro and macro scales. In this study, four nano-sized graded absorbents are explored by wrapping carbonyl-iron powder with nano-sized silicon dioxide. The micro-scale wrapping effects are assessed via a scanning electron microscopy experiment to identify the best sample, including a 5 wt% coupling agent and a 10 wt% dispersant in CIP ethanol. The 10 mm thick concrete matching layer containing the graded absorbent is built using spraying methodology, and the 15 mm thick absorption layer is fabricated using extruding methodology. The mercury intrusion porosimetry experiments show that the sprayed matching layer incurs maximum porosity at 19.8% while the 3D extruded layer ensures the minimum porosity at 11.13%, strengthening the laminar grading effect to improve the EMW impedance matching. The Naval Research Laboratory experiments corroborate that double-layer structures improve the EMW-reflecting capacity compared to their single-absorption-layer counterparts and that the double-layer element containing the best wrapping effect absorbent yields the best reflection loss value, with a minimum of −14.7 dB and a 9.72 GHz effective bandwidth. The EMW-reflecting mechanism for the novel graded double-layer structure containing the graded nano-sized absorbent is then summarized. Theoretical calculations using the transform line theory suggest that a 15 mm absorbing layer and a 15 mm impedance matching layer is specified as the most optimized configuration, with −10.47 dB in average reflection, −15.18 dB in maximum RL, and 9.56 GHz in effective bandwidth. [ABSTRACT FROM AUTHOR]

In the manufacturing process of 3D Concrete Printing (3DCP), defects and anomalies have a significant impact on both the success rate and the quality of the final products, underscoring the need for real-time monitoring. Currently, monitoring is primarily based on manual observation and existing automated methods are limited in real-time performance and accuracy. This study introduced a real-time and highly accurate defect detection and measurement system for using deep learning (DL) and computer vision (CV) techniques. A range of improvement methods were applied in YOLOv7, showing better capacities of accuracy and speed for detecting defects in 3DCP than current cutting-edge detectors such as YOLOv8. Notably, the virtual high-fidelity data were produced by DL based data augmentation strategy and their effects were assessed. Replacing real data as the training dataset, the generated virtual data were used in the models to improve measurement accuracy. Applying the proposed method, the comprehensive insights into 3DCP defects were obtained. Consequently, the relationship formula between defect frequency and printer parameters was investigated by the proposed method, guiding operators in effectively controlling printer parameters and preventing breakpoint defects during the printing process. • An innovative model based on dual deep learning technologies. • A range of improved methods used in the detector. • High-precision detection and measurement of 3DCP anomalies. • The relationship formula between defect frequency and printer parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • Steam curing parameters (temperature rise rate, sustained temperature time, and sustained temperature) have a great influence on compressive properties of 3D printed concrete. • Anisotropy, a crucial characteristic of 3D printed concrete can be significantly influenced by steam curing. • Machine learning technology shows accurate and reliable performance in predicting compressive strength considering anisotropy and steam curing condition. • Beetle antennae search automatically tunes the hyperparameters of machine learning models. The 3D concrete printing (3DCP) technique piques the curiosity of several researchers and enterprises. However, there are few systematic investigations into how curing conditions influence the mechanical performance of 3DCP. This study aims to investigate the effect of various steam curing conditions (temperature rise rate, retention capacity, and sustained temperature) on the performance properties of 3D printing concrete materials at various ages of curing. A thorough test comprises macroscopic and microscopic analysis was conducted. In addition, the best conditions for steam curing are established for compressive characteristics in different directions. Then the anisotropy of mechanical properties of printed materials are studied under various curing settings. This study has contributed to the theoretical research on the influence of steam curing conditions on printed components. In addition, the experimental results were used to create two machine learning (ML) models and the beetle antennae search (BAS) technique was utilised. According to test data, the model is carried out to achieve the mechanical performance prediction of steam curing concrete. To automatically find optimal hyperparameters of ML models, the BAS algorithm was proposed, providing a solid guarantee for the rapid construction of the model. [ABSTRACT FROM AUTHOR]

• The rubber-modified recycled aggregate concrete (RRAC) can be fabricated for low-carbon sustainability. The incorporation of RA and RPs can reduce the strength loss when concrete is exposed to high temperature. • Enhanced peak strain can be obtained through increasing the content of RA and RPs. The properties of RRAC are sensitive to the exposed temperature. • The hyperparameters of machine learning (ML) models were successfully tuned by beetle antennae search (BAS) algorithm. The established ML models possessed high accuracy and good generalisation performance. • The BPNN model possessed highest R value and lowest RMSE value compared to RF, LR, and MLR models, thus it had best prediction performance on the database in this study. The recycled aggregate (RA) and waste rubber particles (RPs) can be combined to prepare rubber-modified recycled aggregate concrete (RRAC) effectively contributing to low-carbon sustainability. However, the mechanical characteristics of RRAC must be investigated before the practical application. To this end, this study focused on the uniaxial compressive strength (UCS) and corresponding peak strain of RRAC with versatile design mixtures (i.e. varying contents of RA and RPs) after exposure to different temperatures ranging from 25 °C (room temperature) to 600 °C. The test results exhibited the negative relationship between UCS and RA replacement ratio, RPs content, and temperature. However, RPs positively affected both the loss of UCS and peak strain when RRAC was exposed to high temperatures. Besides, four machine learning (ML) models were developed based on a relatively comprehensive dataset including 120 groups of experimental results. The beetle antennae search (BAS) algorithm was applied to tune the hyperparameter of ML models. The high correlation coefficients (0.9721 for UCS and 0.9441 for peak strain) were determined in modelling using back propagation neural network (BPNN), presenting its accuracy and reliability. Furthermore, BPNN possessed optimal prediction performance since the lower root mean square error (RMSE) and higher correlation coefficient were obtained compared to the other three ML models (random forest, logistic regression, and multiple linear regression). [ABSTRACT FROM AUTHOR]

• Compressive strength varies with AT levels and specimen orientation, highlighting the complex factors influencing concrete strength. • Highest strength of 105 MPa is found in 75 % AT mold-cast due to improved microstructure from AT-cement reactions. • Flexural strength shows variations across orientations and AT concentrations, improving up to 75% AT due to matrix integration. • Porosity changes with AT levels; more AT increases water absorption and pore formation due to higher silicon dioxide content. • GHG emissions drop by 8.63% with 100% AT; 75% AT best balances sustainability with mechanical strength in 3DPC. 3D printed concrete (3DPC) is an innovative solution offering faster construction, reduced waste, and greater design flexibility. Antimony tailings (AT) enhance the material's durability and strength while promoting sustainability by reducing waste. This provides a cost-effective and eco-friendly solution for construction. Based on it, this research tries to verify the viability of a 3D printing concrete with up to 100 % AT by assessing its fluidity, extrudability, buildability, and strength. This study used five different replacement percentages (0 %, 25 %, 50 %, 75 % and 100 %) of AT to partially replace the silica sand in the mix. Additionally, 1 % of the polyvinyl alcohol (PVA) fibers were used to improve the flexural capacity of the mixes because of the non-availability of the reinforcement in the 3DPC. Results showed that addition produced the highest compressive and flexural strength at 75 %AT. There was a smaller reduction in the compressive and flexural strength at 75 %AT anisotropically. The highest porosity was observed in the 100 %AT mix due to higher water absorption than control. This shows that the mix with 75 %AT can not only meet the printability requirements but can provide better strength which is beneficial for the practical application of 3D concrete printing. [ABSTRACT FROM AUTHOR]

Highlights • The compressive strength of lightweight self-compacting concrete was modelled intelligently. • Beetle antennae search algorithm was firstly used to tune hyper-parameters of random forest. • The importance of different input variables was measured. Abstract Self-compacting concrete (SCC) can achieve compaction into every part of the formwork through its own weight without any segregation of the coarse aggregate. Lightweight concrete (LWC) can reduce the dead load of the structure by incorporating the lightweight aggregate (LWA). In recent years, more and more studies have focused on combining the advantages of SCC and LWC to produce lightweight self-compacting concrete (LWSCC). As one of the most important mechanical properties, uniaxial compressive strength (UCS) values need to be tested before field application of this new material. However, conducting UCS tests with multiple influencing variables is time-consuming and costly. To address this issue, this paper proposed, for the first time, a beetle antennae search (BAS) algorithm based random forest (RF) model to accurately and effectively predict the UCS of LWSCC. This model was developed and verified using data from LWSCC laboratory formulation. Results show that BAS was efficient in searching the optimum hyper-parameters of RF. The proposed BAS-RF model achieved high predictive accuracy indicated by a high correlation coefficient (0.97). In addition, by measuring the variable importance, we conclude that temperature was the most sensitive to UCS development, followed by scoria content and water-to-binder (w/b) ratio, while UCS was less sensitive to fiber content. This pioneering work provides a simple and convenient method for evaluating UCS of LWSCC at varying temperatures. [ABSTRACT FROM AUTHOR]

• The MD models of the interface between waste-oil recycled asphalt and aggregate were characterized and established. • The evolutionary process of dynamic water intrusion into recycled asphalt molecular was revealed. • The deterioration mechanism of recycled asphalt-aggregate interface adhesion was demonstrated. The integrity of the asphalt-aggregate interface is challenged by the dynamic water pressure under traffic loads, especially for recycled asphalt mixtures that represent lower moisture damage resistance by comparing with virgin asphalt mixtures. The effect of dynamic water pressure on the microscale interfacial adhesion properties between the waste-oil recycled asphalt and aggregate was comprehensively investigated based on the molecular dynamic simulation. The molecular models of recycled asphalt and aggregate were calibrated based on the chemical and physical analysis in experiments. The interfacial contact models between recycled asphalt with different regenerants (waste cooking oil-WCO, waste vegetable oil-WVO, and waste engine oil-WEO) and basalt aggregate were established, which also incorporated the thermodynamic process of water intrusion into the interface. The interface behaviors were studied by analyzing the cohesion energy of asphalt, the adhesion energy of interface, and the distribution of asphalt and water concentration on the aggregate surface. The results demonstrated that the dynamic water pressure can accelerate the water to penetrate the asphalt film. The recycled asphalt was pushed away from the basalt aggregate surface after the water film was formed at the interface, where the four components of asphalt leave the interface at different rates. Compared with the other two kinds of waste-oil recycled asphalt and conventional recycled asphalt, the WEO recycled asphalt-aggregate interface represented the optimum moisture damage resistance. The decrease of interface adhesion energy is about 55.7 % subjected to the coupling effects of the high-temperature and dynamic water pressure at 0.5 MPa. [ABSTRACT FROM AUTHOR]

• The cement content and curing time positively affect the mechanical properties of GRFP tendon reinforced cemented soil comprising unconfined compressive strength (UCS) and ultimate pullout strengths (T p), whereas the water content generally has a negative effect. • The 10-fold cross-validation and beetle antennae search (BAS) effectively tune the hyperparameters of the support vector regression (SVR) models for UCS and T p. • The outstanding performance of the BAS-SVR model is shown in high correlation coefficients (UCS: 0.988, T p : 0.972) and low RMSE values (UCS: 313 kPa, T p : 167 kPa). • The multi-objective optimisation design solusions for UCS-cost and T p -cost are both successfully obtained through generating Pareto fronts based on MOBAS-SVR. • Sensitivity analysis determines the most significant varible among all the input features is cement content. Rebar reinforced cemented soil is employed widely to solve the weak foundation problem led by sludge particularly. Nowadays, the glass fiber-reinforced polymer (GFRP) becomes a new tendon material instead of steel to avoid the performance degradation resulting from steel corrosion. The interface bond strength of GFRP tendon-reinforced cemented soils (GTRCS) displays its excellent mechanical capacity. Nevertheless, its application is obstructed by the deficient studies between the bond strength and influence factors. Therefore, this study investigates the effects of varying water contents (C w : 50%-90%), cement proportions (C c : 6%-30%), and curing periods (T c : 28 days, 90 days) on both pullout strength (T p) and unconfined compression strength (UCS) of GTRCS. The results showed that the pullout strength and compressive strength were positively related to T c and C c and negatively related to C w. Besides, these experimental results were also utilised to develop support vector regression (SVR) models. The beetle antennae search (BAS) algorithm was used to adjust the SVR's hyperparameters. The high correlation coefficients (0.988 for UCS and 0.972 for T p) proved the reliability of the established BAS-SVR models. In addition, the multi-objective beetle antennae search algorithm (MOBAS-SVR) was developed for bi-objective optimisation designs (UCS-cost and T p -cost). Finally, sensitivity analysis was conducted to range the significance of variables for T p and UCS. [ABSTRACT FROM AUTHOR]

• A novel fibre-jacket-reinforced mould is developed for 3D printing application. • Effect of hybrid fibre on tensile and flexural properties is investigated. • A properly designed printing path can improve the strength of 3D-printed mortar. • A model is developed to predict the bending capacities of the reinforced specimens. Recently, 3D printing technology has been rapidly developing in the construction sector around the world. However, the insufficient tensile and flexural strengths of the printed material is a crucial problem due to the difficulty of applying steel reinforcement. As a printable material, fibres can be added to the mixture to improve the post-cracking behaviour and toughness of reinforced mortar. Therefore, the present study proposes a 3D-printed fibre-reinforced mortar frame with basalt and carbon fibres to develop a fibre-jacket-reinforced specimen. Specimens with different numbers of reinforcing layers (two, four and six layers) and four different printing paths were fabricated to investigate the strengthening effect on both flexural and tensile capacities. Results from both tests show that a properly designed printing path can significantly improve the tensile and flexural strengths of 3D-printed mortar due to the fibre alignment effect. The strength of fibre-jacket-reinforced specimens is enhanced to a different extent compared with the strength of cast specimens without fibre (with maximum flexural strengths of up to 167% and splitting tensile strengths of up to 128% compared with the respective strengths of the control groups). Meanwhile, both strengths of the fibre-jacket-reinforced specimens with six reinforcement layers almost reach the same strengths of a fully printed fibre-reinforced specimen, but the amount of fibre is reduced by half. In addition, an electromechanical impedance (EMI) technique is applied to monitor the damage during the loading process and an analytical model is developed to predict the enhanced bending capacities of the proposed fibre-jacket-reinforced specimens. [ABSTRACT FROM AUTHOR]

• A multi-output model was firstly applied for predicting pervious concrete properties. • A bio-inspired algorithm was modified to tune hyperparameters of the multi-output model. • A number of pervious concrete samples were prepared in laboratory to test the model. Pervious concrete can purify water, mitigate storm water runoff and reduce the urban heat island effect due to its larger porosity. However, its highly porous inner structure causes a lower compressive strength in comparison with normal concrete. Therefore it is vital to accurately predict the two basic parameters: permeability coefficient (PC) and uniaxial compressive strength (UCS) before field application to reduce time and cost of a construction project. As traditional mathematical models cannot model the highly nonlinear relationships between PC (or UCS) and its constituents, this study addresses this problem by applying a hybrid artificial intelligence model: multi-output least squares support vector regression (MOLSSVR). This model can also improve the prediction accuracy by utilizing the relationship between the two outputs: PC and UCS. In addition, the hyperparameters of MOLSSVR are tuned by a beetle-antennae search (BAS) algorithm which is modified by incorporating self-adaptive inertia weight and Levy flight. To train the proposed model, a large number of pervious concrete samples with different mixture proportions were prepared in laboratory. The results show that the searching efficiency of the modified BAS is significantly higher than that of BAS. The proposed hybrid model achieves better prediction accuracy than other models in the literature. This method can be used to address other multi-output problems in civil engineering. [ABSTRACT FROM AUTHOR]