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Cr(VI) present in effluent discharged from tannery, electroplating, chemical industries, etc., has led to Cr(VI)-induced environmental contamination, which is threatening the ecosystem and human health. Conventional Cr(VI)-detecting techniques are inefficient with expensive instrumentation and additional chemical compounds. To address these issues, sensors can be employed for Cr(VI) detection due to their advantages of higher selectivity, sensitivity and responsibility. This review paper firstly summarises the properties, industrial use and toxicity of Cr(VI), as well as traditional detecting techniques. Then, we describe the principles and types of electrochemical sensors (potentiometric sensors, amperometric sensors and conductometric sensors) and non-electrochemical sensors (optical sensors and microcantilever sensors) in detail for detection of Cr(VI). Challenges and examples for each of the major sensor related to different application areas are reviewed. This review paper aims to act as a reference for researchers in developing new sensors for detection of Cr(VI). [ABSTRACT FROM AUTHOR]

Superhigh‐water backfill mining can mitigate rockburst risks in rockburst‐prone deep coal mines and reduce pollution caused by traditional cemented paste backfill. In this mining method, the width of the rib pillar between two longwall panels is important to maintain roadway stability, prevent rockbursts and water seepage. To design the width of rib pillars, this study firstly established a T‐shaped overlying strata structure model and analyzed source of stress that caused deformation of the T‐shaped model. Based on this model, the abutment stress in the rib pillar was determined. Then, the criterion for overall burst instability of the rib pillar was proposed according to the derived abutment stress. The limit equilibrium theory was applied to obtain the pillar plasticity which can be used as the criterion for water‐seepage prevention. The proposed approach was used to design the width of a rib pillar in Yineng Coalmine located in Shandong Province, China. The analysis of the microseismic monitoring results and borehole drill cuttings show that the designed rib pillar with a width of 10 m was stable without water seepage during mining, indicating the width design method proposed in this study is effective. [ABSTRACT FROM AUTHOR]

Objective A positive result of SARS-CoV-2 nucleic acid detection provides critical laboratory evidence for clinical confirmed diagnosis, pandemic status evaluation, a pandemic prevention plan, treatment of infected people with symptoms, and protection of uninfected people. This study aims to provide a practical reference for SARS-CoV-2 nucleic acid–related research and detection. Methods Our laboratory has established policies combining personnel management and quality control practices for SARS-CoV-2 nucleic acid detection during the pandemic. Results In this article, we describe cross-department personnel management and key points of personal protection and quality control in the testing process. We also report on the differences in detection and the compatibility between different brand kits. Conclusion It is critical to maintain a standard and accurate laboratory operation for nucleic acid testing. [ABSTRACT FROM AUTHOR]

Fluoride phosphors as red components for warm white LEDs have attracted a tremendous amount of research attention. But these phosphors are extremely sensitive to moisture, which seriously limits their practical industrial applications. To tackle this problem, unlike all the straightforward preventive strategies, a reverse strategy "Good comes from bad" was successfully developed to treat the degraded K2SiF6 : Mn4+ (D‐KSFM) phosphor in the present study, which not only completely restores the luminescence properties, but also significantly enhances the moisture resistance at the same time. After treatment with an oxalic acid solution as restoration modifier, the emission intensity of the D‐KSFM phosphor can be restored to 103.7% of the original K2SiF6 : Mn4+ red phosphor (O‐KSFM), and the moisture resistance is remarkably improved. The restored K2SiF6 : Mn4+ (R‐KSFM) maintains approximately 62.3% of its initial relative emission intensity after immersing in deionized water for 300 min, while the reference commercial K2SiF6 : Mn4+ with a protective coating (C‐KSFM) is only 33.2%. As a proof of general applicability, this strategy was also conducted to K2TiF6 : Mn4+ phosphor, which is less moisture‐stable than K2SiF6 : Mn4+. The luminescence intensity of the degraded K2TiF6 : Mn4+ (D‐KTFM) phosphor can be restored to 162.6% of original level of the K2TiF6 : Mn4+ synthesized through a cation exchange approach without any treatment (O‐KTFM). The emission intensity of the restored K2TiF6 : Mn4+ (R‐KTFM) phosphor retains 62.8% of its initial emission intensity after soaking in deionized water for 300 min. Finally, the R‐KSFM phosphors were packaged into white light‐emitting diodes with blue InGaN chips and Y3Al5O12 : Ce3+ yellow phosphors. The WLEDs display excellent color rendition with higher color rendering index, lower color temperature (WLED‐II: Ra=83.6, R9=57.3, 3743 K, ηl=199.68 lm/W; WLED‐III: Ra=90.4, R9=94.2, 2892 K, ηl=183.3 1 m/W). The above results show that the reverse strategy can be applied in those phosphor materials with poor moisture resistance to restore luminescence properties and improve moisture resistance without excessively care about the deterioration during the production, storage and transportation. [ABSTRACT FROM AUTHOR]

Roadway stability in extremely soft coal mass is a typical challenge in deep underground mines, as the most widely adopted support structure such as rock bolting, compound support system normally fails. This paper proposed a novel prereinforcement method for coal roadway, that is, jet grouting (JG) technique, to improve the quality of surrounding soft coal mass and form a stable support structure namely jet grouted coalcrete. The field and laboratory tests were conducted for evaluating the applicability of JG and the mechanical parameters of coalcrete. A numerical model was verified by comparing the calculated results with field measurements. According to the confirmed model, three typical JG support schemes were performed to assess the roadway stability after JG. The results showed that the proposed JG schemes can reduce the deformation and failure zone of the roadway and optimize the stress states around the roadway. The mechanism related to these advantages was investigated. This study provides a promising idea for supporting soft coal roadway in deep underground coal mines, which can promote the JG application in the field. [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]

• 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]

Abstract The coalcrete, a new supporting material produced by jet grouting (JG) technique was firstly studied for improving soft coal mass to support roadways in Guobei coal mine, China. Young's modulus is an essential indicator to evaluate the deformation-resisting ability of coalcretes. In this study, for determining Young's modulus of coalcretes efficiently, an intelligent technique was proposed using the support vector machine (SVM) and beetle antennae search (BAS). The hyper-parameters of SVM were firstly tuned by BAS, and then the SVM-BAS model with optimum hyper-parameters was employed to model the non-linear relationship between the inputs (coal content, water content, cement content, and curing time) and output (Young's modulus). By combining these variables, 360 coalcrete samples in total were prepared and tested for establishing the dataset. The results show that BAS is more reliable and efficient than the trial–and–error tuning method. Moreover, by comparison with other baseline models such as back-propagation neural network (BPNN), logistic regression (LR) and multiple linear regression (MLR), the optimized SVM-BAS model is more reliable, accurate and less time consuming for predicting Young's modulus of coalcretes. Besides, by conducting sensitivity analysis (SA), the importance of different input variables was determined. This pioneering work provides guidelines for predicting Young's modulus of coalcretes and designing proper JG parameters in engineering applications. Highlights • The jet grouting technique was firstly utilized in underground coal mine for generating coalcrete to reinforce the roadway stability • A total of 360 specimens were tested to determine the deformation-resisting ability of coalcrete. • A support vector machine model was proposed for predicting Young's modulus of coalcrete. • The hyperparameters of support vector machine were tuned by beetle antennae search algorithm. [ABSTRACT FROM AUTHOR]

The addition of a red-emitting phosphor to YAG:Ce3+-based white light-emitting diodes (WLEDs) greatly facilitates their applications in the field of high-color-rendering-index warm solid-state lighting. It is highly desirable to develop a red phosphor with satisfactory spectral features and low synthesis cost. In this study, a novel non-rare-earth and nonequivalent doping type of Cs2NbOF5:Mn4+ oxyfluoride red-emitting phosphor with high luminous efficiency was obtained via a facile room-temperature co-precipitation method, and its morphology and luminescent properties were investigated in detail. The Cs2NbOF5:Mn4+ phosphor with micro-rod-like morphology exhibited broad band absorption at blue light region (∼474 nm) and narrow bandwidth emissions at red region (∼633 nm). The color purity of the Cs2NbOF5:Mn4+ phosphor was calculated to be about 99%, and the internal quantum yield (QY) under 474 nm excitation was 63.4%. The concentration quenching of Mn4+ in Cs2NbOF5 matrix was mainly due to dipole–dipole interactions, and the activation energy of temperature quenching was calculated to be ∼0.2610 eV. The demonstration of a blue InGaN LED chip in combination with a blend of newly developed Cs2NbOF5:Mn4+ red phosphor and YAG:Ce3+ yellow phosphor greatly decreased the correlated color temperature (CCT) from 6255 to 3517 K while significantly improving the color rendering index (CRI) from 72.5 to 87.5. It deserves to be mentioned that the brand-new matrix to phosphor in the present study can be extended to various niobium/tantalum oxyfluoride series, which is very helpful for developing and designing new red phosphors. [ABSTRACT FROM AUTHOR]

Abstract Lack of red-light of current w-LEDs restricts their applications in the field of high color rendering index warm solid-state lighting. Fluoride phosphors doped with Mn4+ provide an efficient way to resolve the problem. Unfortunately, these excellent Mn4+ activated fluoride red phosphors are generally synthesized in highly toxic HF solution. In this paper, we report a novel green synthetic route to prepare a non-rare-earth and nonequivalent doping type of K 2 NbF 7 :Mn4+ red phosphor with micro-rod-like morphology. The results of XRD and its Rietveld refinement reveal that all the samples possessed only a single phase with monoclinic P21/c structure and the Mn4+ ions were successfully incorporated into the crystal lattice. The Mn4+ ions in K 2 NbF 7 matrix have a highly distorted octahedral environment, which strengthen the intensity of zero phonon line (ZPL). The strongest absorption and excitation bands appear in blue region of diffuse refection spectra (DRS) and the photoluminescence excitation (PLE) of K 2 NbF 7 :Mn4+ sample, which demonstrates the product perfectly match with the most popular GaN-based blue chip. Under blue light illumination, the as-prepared K 2 NbF 7 :Mn4+ sample exhibits intense sharp-line red fluorescence (∼628 nm) with internal quantum yield (QY) of 71.3%, an extreme narrow full width at half-maximum (FWHM) of ∼2.8 nm, and an ultra-high-color purity of 99.2%. Impressively, by incorporating K 2 NbF 7 :Mn4+ as a red component, a warm w-LED with high CRI of 83.8, low color temperature of 3010 K and high luminous efficiency of 115.91 lm/W is obtained, demonstrating great validity of K 2 NbF 7 :Mn4+ red phosphor for white LED devices. Highlights • The K 2 NbF 7 :Mn4+ red phosphor with high luminous efficiency was obtained successfully via a novel HF-free approach. • Reaction mechanism of K 2 NbF 7 :Mn4+ red phosphor, including incorporation mode of Mn4+ into K 2 NbF 7 host, were investigated carefully. • Optical properties of K 2 NbF 7 :Mn4+ red phosphor, especially the intense ZPL emission, were investigated carefully. • Addition of K 2 NbF 7 :Mn4+ red phosphor, a warm w-LED with high CRIand low color temperature is obtained. [ABSTRACT FROM AUTHOR]

The investigation on single phase multi-color phosphors is highly meaningful for near-ultraviolet chip based white light emitting diodes. In this work, a series of Eu 2+ and Tb 3+ singly doped and Eu 2+ /Tb 3+ codoped Sr 5 (PO 4 ) 3 Cl phosphors were synthesized via a high-temperature solid state reaction method. The luminescence spectra and decay curves of Eu 2+ and Tb 3+ singly doped samples were discussed, the optimal doping concentrations were determined. Thanks to the spectra overlap between Eu 2+ and Tb 3+ , nonradiative energy transfer from Eu 2+ to Tb 3+ was investigated. It is found electric dipole-dipole interaction played the main role for the energy transfer in codoped samples, the highest energy transfer efficiency was calculated to be 60.98%. Tunable emissions are observed for codoped samples by adjusting doping concentration. The thermal quenching properties were discussed and the activation energy (ΔE) was estimated in the present work. [ABSTRACT FROM AUTHOR]

Tourism eco-efficiency plays an important role in the environment due to serious environment pollution and resource consumption. An evaluation of the tourism eco-efficiency of 53 Chinese coastal cities was conducted for 2003–2013, exploring the overall efficiency levels, regional differences, eco-efficiency types and influencing factors. To achieve the objectives of the study, a data envelopment analysis (DEA)-Tobit model is applied. The major findings indicate that the overall tourism eco-efficiency of Chinese coastal cities is 0.860, which means that only a few cities are effectively efficient and that most cities still have room for improvement. This finding implies that tourism development has a more negative impact on the environment and that there are substantial regional differences in efficiency. The results also show that economic and ecological indicators have significantly positive influences on the tourism eco-efficiency of Chinese coastal cities, while the number of tourists and the use of three major pollutants in the tourism industry have a negative impact. We identify several influencing factors and propose several suggestions for increasing tourism eco-efficiency and the development quality of Chinese coastal cities. [ABSTRACT FROM AUTHOR]

Lightweight concrete (LWC) is widely used in the construction industry due to a variety of advantages. However, compared with traditional normal-weight concrete, more influencing variables (e.g. types of lightweight aggregates) must be considered to optimize multiple properties including uniaxial compressive strength (UCS), density and cost. This makes the mixture design of LWC more difficult or sometimes impossible using laboratory experiments. To address this issue, this study proposes a multi-objective optimization (MOO) method using machine learning and metaheuristic approaches for LWC mixture design through a two-step approach. In the first step, a least squares support vector regression (LSSVR) model is constructed to predict multiple properties of LWC. The hyper-parameters of the LSSVR model are tuned using the firefly algorithm (FA). A dataset containing a large number of different mixtures of LWC is compiled from published literature. High prediction accuracy (0.97 for UCS and 0.90 for density) is achieved on the test dataset (including 30% of all the instances). In the second step, a newly developed multi-objective FA (MOFA) model is used to optimize the LWC mixture, while satisfying the constraints. The Pareto fronts of the triple objectives (UCS, cost and density) are successfully obtained. The proposed MOO method is powerful and efficient in finding optimal LWC mixtures with conflicting objectives and therefore decision making can be facilitated in early phases of construction. [ABSTRACT FROM AUTHOR]

The accumulation of coal gangue poses significant environmental challenges due to its massive volume and low reactivity. This study investigates the influence of calcium hydroxide (Ca(OH)₂) and gypsum on the mechanical properties, hydration, and pore structure of low-carbon cementitious materials incorporating high volumes of calcined coal gangue (CCG) and limestone. The results showed that the addition of Ca(OH)₂ significantly increased compressive strength, with a 19 % improvement at 28 days in LCC-40-P mix, which contained 40 % cement and 2.73 % Ca(OH)₂ by weight. The incorporation of 10 % gypsum in the LCC-40-P mix and 20 % gypsum in the LCC-70 mix (containing 70 % cement without Ca(OH)₂) effectively refined the pore structure, reduced overall porosity, and enhanced the material's density and mechanical properties, resulting in compressive strengths of 38.4 MPa (72 % of the strength of Portland cement) and 50.2 MPa (94 % of the strength of Portland cement) at 28 days, respectively. Microstructure and hydration heat tests showed that the addition of Ca(OH)₂ accelerates the hydration reaction and significantly enhanced compressive strength by ensuring sufficient Ca(OH)₂ for pozzolanic reactions, leading to the formation of additional calcium silicate hydrate, calcium aluminate silicate hydrate and carboaluminate phases. Gypsum refined the pore structure by shifting the pore size distribution towards smaller sizes, reducing overall porosity, and increasing the proportion of gel and small capillary pores. These findings highlight the potential of using CCG with optimized additives to develop sustainable, low-carbon construction materials with enhanced performance characteristics. • Improved compressive strength in cementitious materials with calcined coal gangue by 19 %. • Gypsum addition refines pore structure, enhancing density and mechanical properties significantly. • Combined use of Ca(OH)₂ and gypsum promotes sustainable low-carbon construction materials. [ABSTRACT FROM AUTHOR]

• The UCS of SFC are predicted using BPNN models. • MOBAS is developed for multi-objective mixture optimization of SFC. • A GUI is developed for mixture optimization of SFC. Partial replacement of cement by silica fume in concrete provides advantages such as mitigation of the impact on the environment of carbon dioxide emitted during cement production, recycling of industrial by-products and improvement of concrete strength and durability. The optimization of the mixture of silica fume concrete (SFC) requires trade-off among multiple objectives (strength, cost and embodied CO 2) and consideration of a large number of variables under highly nonlinear constraints. Obtaining the Pareto front of this multi-objective optimization (MOO) problem is computationally expensive. To address this issue, the present study develops a MOO model using machine learning (ML) techniques and a new meta-heuristic algorithm. Firstly, the relationships between components and SFC properties are modelled on a dataset using a back propagation neural network (BPNN) model. Then an individual-intelligence-based multi-objective beetle antennae search algorithm (MOBAS) is developed to search for optimal SFC mixtures that maximize UCS, and minimize cost and embodied CO 2 under defined constraints. Results indicate that the proposed MOBAS is more computationally efficient with satisfactory accuracy in comparison with algorithms based on swarm intelligence. The MOO model achieves reliable predictions for UCS with a very high correlation coefficient (0.9663) on the test set. The Pareto front of optimal SFC mixture proportions of the MOO problem is successfully obtained using the proposed model. The proposed frame-work improves the efficiency in SFC mixture optimization and can facilitate appropriate decision making before construction. [ABSTRACT FROM AUTHOR]

• The strength of SFRC are predicted using FA-SVR; • Multi-objective firefly algorithm is proposed based on the basic firefly algorithm; • The mixture proportion of SFRC is automatically designed using a multi-objective optimization method. Steel fibre reinforced concrete (SFRC) is widely used in the construction concrete industry as it partakes an important role of evolving concrete technology. It consists of steel fibres of various shapes, sizes and geometries that influence the concrete mix composition and mechanical properties. However, compared to traditional concrete, it is difficult to design the mix proportions because more influencing variables need to be considered to optimise multiple properties including ultimate compressive strength, tensile or flexural strength and cost. Therefore, the present study proposes an artificial intelligence based multi-objective optimization model to enable an efficient method of finding the optimum mix design for SFRC. A large dataset including 299 instances for uniaxial compressive strength (UCS) test and 269 instances for flexural strength (FS) test were collected from previous literature. Support vector regression (SVR) model was applied to predict UCS and FS for SFRC. The hyper parameters of SVR models were tuned using a firefly algorithm (FA) and a sensitivity study was conducted to understand the importance of the inputs on the output variables for the algorithms. High correlation coefficients (0.91 for UCS and 0.85 for FS) were achieved on the test dataset. The FA-SVR model was then applied as the objective function for a developed multi-objective FA to search for the optimal SFRC mixture proportion. Pareto optimal solutions were obtained and served as a design guide to determine the optimal SFRC mixtures. [ABSTRACT FROM AUTHOR]

The replacement of natural coarse aggregate (NCA) with recycled coarse aggregate (RCA) in concrete mixtures offers various advantages, including conservation of natural resources, reduction of CO 2 emissions, and cost reduction. However, multiple related variables and objectives (e.g., mechanical, economic, and environmental objectives) need to be considered when optimizing mixtures of recycled aggregate concrete (RAC). This cannot be achieved through traditional laboratory- or statistics-based methods. This study proposes a hybrid intelligent system based on artificial intelligence (AI) and metaheuristic algorithms for designing optimal mixtures of RAC. To verify the proposed model, a data set containing 344 different RAC mixtures was collected from previous literature. A semi-supervised cotraining algorithm using two k -nearest neighbor (k NN) regressors with different distance metrics is developed to label the unlabeled data in the collected dataset. Different AI models are incorporated into the system for modeling the relationship between RAC strength and its influencing variables. A multi-objective optimization (MOO) model based on AI algorithms and on a multi-objective firefly algorithm is used to search for optimal mixtures of RAC. The results show that k NN-based semi-supervised cotraining can effectively exploit unlabeled data to improve the regression estimates. In the test set, A Random Forest and Backpropagation Neural Network achieve the best prediction accuracy for predicting, respectively, uniaxial compressive strength and splitting tensile strength of RAC, indicated by the highest correlation coefficients (0.9064 and 0.8387, respectively) and lowest root-mean-square errors (6.639 MPa and 0.5119 MPa, respectively). The Pareto fronts of the multi-objective mixture optimization problem are successfully obtained by the MOO model. The proposed system can also be used to optimize mixture proportions of other cementitious materials in civil engineering. • RF and BPNN achieve highest accuracy for predicting UCS (R = 0.91) and STS (0.84) of RAC, respectively. • The kNN-based semisupervised cotraining can effectively exploit unlabeled data to improve the regression estimates. • Pareto fronts of multi-objective mixture optimization problem for RAC are obtained by using AI models and MOFA. [ABSTRACT FROM AUTHOR]

• BPNN has good prediction accuracy for UCS, while RF performs better in predicting slump. • PSO is efficient in tuning hyperparameters of machine learning models. • The Pareto front of the mixture optimization problem is obtained by MOPSO. For the optimization of concrete mixture proportions, multiple objectives (e.g., strength, cost, slump) with many variables (e.g., concrete components) under highly nonlinear constraints need to be optimized simultaneously. The current single-objective optimization models are not applicable to multi-objective optimization (MOO). This study proposes an MOO method based on machine learning (ML) and metaheuristic algorithms to optimize concrete mixture proportions. First, the performances of different ML models in the prediction of concrete objectives are compared on data sets collected from the published literature. The winner is selected as the objective function for the optimization procedure. In the optimization step, a multi-objective particle swarm optimization algorithm is used to optimize mixture proportions to achieve optimal objectives. The results show that the backpropagation neural network has better performance on continuous data (e.g., strength), whereas the random forest algorithm has higher prediction accuracy on more discrete data (e.g., slump). The Pareto fronts of a bi-objective mixture optimization problem for high-performance concrete and a tri-objective mixture optimization problem for plastic concrete are successfully obtained by the MOO model. The MOO model can serve as a design guide to facilitate decision-making before the construction phase. [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]

Prediction of rockburst in underground engineering is of significance as it is close to the safety of support structures, personnel, and working environments. To improve the accuracy of rockburst classification, this paper proposed an ensemble classifier RF-FA model in which the random forest classifier (RF) and firefly algorithm (FA) were combined to achieve the optimum performance on rockburst prediction. Five key parameters of surrounding rock, i.e., the depth H, the maximum tangential stress σθ, the uniaxial compressive strength σc, the tensile strength σt, and the elastic energy index Wet, are selected as input variables while the rockburst intensity including none, light, moderate, and strong classes was chosen as output. A total of 279 cases worldwide were collected and used for train and test the proposed RF-FA model. The results showed that the FA can optimize the hyperparameters of RF efficiently and the optimum model exhibited a high performance on rockburst data from the independent test set and new engineering projects. The feature importance obtained by the ensemble RF-FA model indicated that the elastic energy index plays the most important role in rockburst. Besides, the proposed model showed much better accuracy on rockburst classification compared with previously existing RF models and empirical criteria, which means it is a useful and robust tool for rockburst prediction. [ABSTRACT FROM AUTHOR]

• High curing temperature increases early concrete strength, while reduces later strength. • The concrete subjected to thermal curing until the CAFS age exhibits superior 28-day UCS in comparison to concrete subjected to thermal curing for only 3 days. • The strength-maturity model corrected using an empirical formula incorporating the "crossover effect", improved the prediction accuracy of concrete strength. The maturity method is widely used to estimate early-age concrete strength. However, the traditional maturity models exhibit limited predictive capability for late concrete strength under thermal curing conditions due to the influence of the "crossover effect". This study developed a curing scheme for Standard Portland cement concrete in the absence of supplementary cementitious materials at temperatures ranging from 5 °C to 50 °C and analyzed the temperature variations inside thermally cured concrete specimens. The findings reveal that an increase in curing temperature and time between 30 °C and 50 °C and 8 and 72 h respectively led to an increase in the early strength and a decrease in the late strength of concrete, due to the "crossover effect". Additionally, a linear relationship was found between curing temperature and the late strength reduction coefficient. Utilizing this relationship, a modified maturity model that considers the "crossover effect" was proposed, improving the accuracy of predicting concrete strength under thermal curing conditions (with a prediction error of less than 10%). The research outcomes are of significant guiding significance for winter construction by ensuring the quality of concrete, reducing construction accidents, and improving construction efficiency. [ABSTRACT FROM AUTHOR]