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The inherent limitations of recycled aggregates restrict the application of recycled concrete in cold regions. In this paper, both steel tube confinement and slurry‐wrapping treatment were employed to improve the frost resistance of recycled concrete. The influence of different aggregate types and number of freeze–thaw cycles on the freeze–thaw effect was investigated experimentally. The results show that with the increase of freeze–thaw cycles, the axial strength of concrete‐filled steel tube (CFST) short columns decreases regardless of aggregate type. The frost resistance of slurry‐wrapping recycled aggregate concrete‐filled steel tube short columns is superior to that of recycled aggregate concrete‐filled steel tube (RACFST) short columns due to slurry‐wrapping treatment; however, they are still not as good as natural aggregate concrete‐filled steel tube short columns. The initial stiffness diminishes as the number of freeze–thaw cycles increases. As the number of freeze–thaw cycles increases, the effect of aggregate type on the ductility of CFST short columns decreases. Based on the test results, a modified formula applicable to the strength of recycled concrete is proposed and combined with Eurocode EC4 to predict the ultimate compressive strength of RACFST short columns under freeze–thaw cycles. [ABSTRACT FROM AUTHOR]

AbstractThe over consolidated clay is widely distributed in the seabed. Monotonic simple shear tests on K0 consolidated clays with an over consolidation ratio (OCR) of 1 ∼ 14 were conducted through a multidirectional cyclic simple shear apparatus. The stress–strain relationship, pore pressure evolution, and shear strength variation under different OCRs were revealed, and differences between over and normally consolidated clay were compared. When OCR is below 8, peak values of shear stress and negative excess pore pressure both increase linearly with increasing OCR. However, the peak values tend to stabilize when OCR is above 8. Correspondingly, the occurrences of peak values no longer delay linearly, and the failure line is also shifted towards stronger strengths. An exponential formula is purposed and validated on accordance with numerous experimental data to describe the aforementioned pattern more precisely compared with the traditional power formula. Therefore, shear strengths of over consolidated clays with various OCRs and consolidation pressures can be accurately predicted according to the exponential formula and the shear strength of a normally consolidated sample with a certain consolidation pressure. [ABSTRACT FROM AUTHOR]

Round-ended concrete-filled steel tubes (RECFSTs) present very different performances between the primary and secondary axes, which renders them particularly suitable for use as bridge piers and arches. In recent years, research into RECFST heavily relies on experimental procedures restricting the parameter range under consideration, which narrows the far-reaching applicability of RECFST. This study employs advanced machine learning methods to predict the axial load-bearing capacity of RECFST with a wide parameter range. Firstly, a machine learning database comprising 2400 RECFSTs is established, which covers a wider range of commonly used material strengths and cross-sectional dimensions. Three machine learning prediction models of this database are then developed, respectively, using different algorithms. The robustness of the machine learning models is evaluated by predicting the axial load-bearing capacity of 60 RECFST specimens from existing references. The results demonstrated that the machine learning models provided superior predictive accuracy compared to theoretical or code-based formulas. A graphical user interface (GUI) is ultimately developed based on the machine learning prediction models to predict the axial load-bearing capacity of RECFST. This tool facilitates rapid and accurate RECFST design. [ABSTRACT FROM AUTHOR]

AbstractAdditive manufacturing (AM) processes, also known as 3D printing, are experiencing growth and more industries are seeking solutions in these types of processes due to their advantages, including reducing the manufacturing time, sustainability, and the freedom to execute complex geometries. However, the dimensions of components are still quite small. Thus, it is necessary to find solutions for cases where assembly and joining of manufactured components are necessary. This work studies the tensile performance of adhesively bonded single-lap joints (SLJ) between AM adherends of polylactic acid (PLA), Polyethylene Terephthalate Glycol-modified (PETG), and Acrylonitrile Butadiene Styrene (ABS), bonded with the adhesives Araldite® 2015 and Sikaforce® 7752. The adherends’ mechanical elastic, plastic and fracture properties are determined prior to the assessment of the adhesive performance in SLJ. Failure modes, joint strength, assembly stiffness, and failure energy are obtained experimentally and compared to Cohesive Zone Model (CZM) predictions, aiming to provide the best material/adhesive combination that maximises the joint performance. In terms of strength and stiffness, PLA joints bonded with the Araldite® 2015 provided the best results, although the behaviour was different for the dissipated energy. The CZM approach showed to be a reliable design approach for bonded AM joints. [ABSTRACT FROM AUTHOR]

Accurately predicting the 28-day compressive strength of Portland cement clinkers is challenging due to their complex multiphase nature, especially when production processes and environmental conditions vary. To address this issue, the TransXRD model was introduced, which is a novel deep learning approach that combines the Transformer architecture with CNN design. 214 clinker samples were collected from 6 cement plants. The diffraction peak sequence of samples ranging from 10° to 55° was used as required input for the model. A combination of one-dimensional convolution is incorporated to extract local features, and self-attention mechanisms are leveraged to perform global feature integration. XRD data were directly utilized, bypassing additional processing to minimize signal loss. Compared to other DNN (deep neural network) models, the TransXRD model demonstrates promising performance, achieving a root mean square error (RMSE) of 1.15, a mean squared error (MSE) of 1.33, a mean absolute error (MAE) of 0.93, and a mean absolute percentage error (MAPE) of 1.76 on the test set. Notably, in the training set, 100% of the data exhibit prediction errors within 2 MPa, while within the test set, 90% of the prediction errors are within 2 MPa. The application potential of XRD data in predicting the compressive strength of cement clinker also offers novel insights into predicting the performance of other materials. [ABSTRACT FROM AUTHOR]

This study focused on investigating the damage development mode and strength prediction model of layered cemented paste backfill (LCPB) using digital image correlation (DIC) and ultrasonic pulse velocity (UPV). The results indicated that the damage mode was associated with the cement-to-tailings (c/t) ratio and the height (h/H) ratio of the middle layer of LCPB. The damage mode of LCPB was mainly tensile damage for a small h/H ratio while increasing the h/H ratio to 0.8 resulted in LCPB exhibiting macroscopic shear damage. With the reduction of the c/t ratio, the damage area of LCPB gradually shifted from the upper layer to the middle layer. The UPV tended to decrease exponentially with both the decrease of the c/t ratio and the increase of the h/H ratio. Comparing the predicted and experimental values, the maximum error of the predicted values was only 0.299, and the value of the a20-index was 0.9792, indicating that the predictive model was reliable. The findings could provide guidance for the design of LCPB. [ABSTRACT FROM AUTHOR]

Shotcrete has been widely used in the excavation of caverns and tunnels as a key load-bearing element in the support system. However, the interfacial stresses and bond strengths within the composite structures formed by the interaction of geologic bodies and tectonic formations are not well understood, which can lead to safety accidents and excessive wastage of support materials in engineering. To address this gap, rock-concrete composite specimens were created using six distinct types of surrounding rocks. The strength law of composite specimens was analyzed, and the uniaxial compressive strength prediction model of composite specimens was derived based on Mohr-Coulomb strength yield criterion. The results show that the uniaxial compressive strength of the composite specimen changes with the change of rock type, which is between the strength of rock and concrete. The uniaxial peak strength and elastic modulus of composite specimens are linearly positively correlated with the ratio (Y) of concrete-rock elastic modulus, which is easily affected by concrete properties. However, the peak strain is linearly and negatively correlated with Y, which is greatly influenced by rock. The rock-concrete interface of the composite specimen plays an important role in the failure of the specimen, which changes the stress transfer state of the composite specimen, produces uncoordinated deformation, and finally causes the failure of the specimen. The theoretical and test value error of the derived composite specimen uniaxial compressive strength prediction model are -1.19%~12.20%, and the theoretical calculation model can be used to predict the uniaxial compressive strength of rock-concrete composite specimens. [ABSTRACT FROM AUTHOR]

Traditional laboratory tests for measuring rock uniaxial compressive strength (UCS) are tedious and timeconsuming. There is a pressing need for more effective methods to determine rock UCS, especially in deepmining environments under high in-situ stress. Thus, this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms. For this purpose, the Lead-Zinc mine in Southwest China is considered as the case study. Rock density, P-wave velocity, and point load strength index are used as input variables, and UCS is regarded as the output. Subsequently, twelve hybrid predictive models are obtained. Root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and the proportion of the mean absolute percentage error less than 20% (A-20) are selected as the evaluation metrics. Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC) achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset. The values of R², A-20, RMSE, and MAE on the training dataset are 0.98, 1.0, 3.11 MPa, and 2.23MPa, respectively. The highest values of R² and A-20 (0.93 and 0.96), and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa, are observed on the testing dataset. The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines. [ABSTRACT FROM AUTHOR]

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the global concrete industry shifts towards sustainable practices, there is an increasing focus on mitigating the environmental impacts of ordinary Portland cement (OPC)-based concrete, which is notorious for its significant greenhouse gas emissions, high energy consumption, and substantial demand for natural resources. This urgency is compounded by the growing production of waste tile ceramic materials due to rapid urban development, leading to enhanced research into their recyclability within concrete for improved durability and sustainability. This study explores the development of high-strength concrete utilizing waste tile ceramic aggregates (WTCAs) and waste tile ceramic powder (WTCP) as replacements for natural aggregate and OPC, respectively. To further enhance the concrete's mechanical properties and microstructure, silica nanoparticles derived from waste bottle glass (WBGNPs) were integrated in varying proportions ranging from 2 % to 10 % of the binder content. Optimal results were achieved with a full replacement of natural aggregate by WTCAs, 60 % substitution of OPC by WTCP, and the inclusion of 4 % WBGNPs, which collectively exhibited superior compressive, splitting tensile, and flexural strengths. Microstructural analyses revealed that the addition of WBGNPs improved the hydration process, increased gel density, and reduced porosity. From the obtained numerical results, the coefficient of determination value of 0.92 further confirms the model's predictive strength, demonstrating that the Random Forest algorithm can reliably estimate the compressive strength. The findings indicate that the concrete formulated with WTCP and WBGNPs not only meets diverse construction demands but also significantly contributes to environmental sustainability by reducing impacts on global warming and minimizing landfill usage. This study advocates for the strategic incorporation of WTCP and WBGNPs in concrete applications to promote environmentally sustainable construction practices. It is highly recommended that WTCP be utilized in sustainable binders as a replacement for OPC and natural aggregates to enhance it strength and durable properties, reduce the environmental issues, costs, and the depletion of natural resources.

Rapid and accurate estimation of rock strength is of great practical significance for the stability of rock engineering. This article is based on the results of triaxial compression tests on crack rocks and comprehensively considers the interrelationships between crack geometric characteristics, confining pressure, and physical and mechanical parameters. A database of rock strength characteristic parameters from an experimental perspective is constructed, and a quantitative prediction model for the strength of crack rock masses under the influence of multiple characteristic parameters is established using the random forest algorithm. The results show that the accuracy of predicting rock strength in categories 2 of 21–30 MPa, 3 of 31–40 MPa, 4 of 41–50 MPa, and 5 of 51–60 MPa is 100%, and the accuracy of predicting rock strength in categories 1 of 11–20 MPa is 82% and in categories 0 of 0–10 MPa is 74%; through the study of parameter correlation and quantitative analysis with strength, the selected parameters are reliable and have a certain degree of correlation, and the influence mechanism of characteristic parameters on rock strength has been explored; the calculation results of parameter classification show that confining pressure and crack length are the key factors affecting rock strength. The importance of other parameters is ranked in the order of longitudinal wave velocity > Poisson’s ratio > saturated water content > porosity > saturated quality > dry quality > saturated density > crack dip angle > crack number > crack penetration. This provides an effective approach to consider the randomness and correlation of rock strength parameters, providing a new idea for rock mechanics research. [ABSTRACT FROM AUTHOR]

The use of adhesive bonding in aeronautical applications has gained popularity due to its numerous advantages over traditional mechanical fastening methods. However, the issue of delamination still poses a significant challenge in fully embracing this technology in the industry. This study proposes a unique approach to optimize carbon fiber reinforced polymer (CFRP) single lap joints (SLJ) by introducing a curved joint design with varying adhesive thickness. The curvature is achieved through the intentional warpage caused by residual stresses resulting from an asymmetrical composite layup. While these residual stresses can potentially lead to cracks and distortions, this research aims to leverage their presence to enhance joint performance and mitigate delamination risks. A comprehensive analysis, involving both numerical simulations and experimental testing was conducted to compare the performance of the curved joint design with traditional configurations. The findings indicate that the curved joint design effectively prevents delamination while maintaining comparable joint strength. This demonstrates the potential benefits of employing curved joints for aeronautical components with curved geometries, affirming the feasibility and advantages of this approach. However, further optimization of the curved joint configuration is warranted to enhance its performance and expand its applicability to a broader range of applications. [ABSTRACT FROM AUTHOR]

Due to the brittle nature of ceramic, the ceramic and construction industry produces a large volume of waste that imposes a severe environmental threat due to its non-biodegradability. In this study, the suitability of ceramic waste as a replacement of natural coarse and fine aggregate in concrete has been investigated by evaluating engineering properties such as bulk density, water absorption, workability, etc. with respect to different concrete samples made with different mix proportions. Furthermore, a prediction model is introduced to predict compressive and splitting tensile strength using the machine learning tool support vector machine (SVM). A data set containing 108 records either for compressive or tensile strength was used for the training and testing purposes of the SVM model. A total of 9 mix proportions was selected and cast cylinders were cured for 7, 28, and 56 days. Four different kernel functions were used to optimize the results and different accuracy parameters like the value of R2, mean absolute error, mean square error, root mean square error, etc. were compared to find the best kernel function for this study. By primarily evaluating the coefficient of determination (R2), SVM showed an acceptable result with an accuracy of over 90%. Moreover, in terms of other accuracy measurement parameters result indicates that the SVM is an effective tool to predict the compressive and splitting tensile strength of concrete comprised of different proportions of ceramic content.

Round-ended concrete-filled steel tubes (RECFSTs) present very different performances between the primary and secondary axes, which renders them particularly suitable for use as bridge piers and arches. In recent years, research into RECFST heavily relies on experimental procedures restricting the parameter range under consideration, which narrows the far-reaching applicability of RECFST. This study employs advanced machine learning methods to predict the axial load-bearing capacity of RECFST with a wide parameter range. Firstly, a machine learning database comprising 2400 RECFSTs is established, which covers a wider range of commonly used material strengths and cross-sectional dimensions. Three machine learning prediction models of this database are then developed, respectively, using different algorithms. The robustness of the machine learning models is evaluated by predicting the axial load-bearing capacity of 60 RECFST specimens from existing references. The results demonstrated that the machine learning models provided superior predictive accuracy compared to theoretical or code-based formulas. A graphical user interface (GUI) is ultimately developed based on the machine learning prediction models to predict the axial load-bearing capacity of RECFST. This tool facilitates rapid and accurate RECFST design.

Due to the brittle nature of ceramic, the ceramic and construction industry produces a large volume of waste that imposes a severe environmental threat due to its non-biodegradability. In this study, the suitability of ceramic waste as a replacement of natural coarse and fine aggregate in concrete has been investigated by evaluating engineering properties such as bulk density, water absorption, workability, etc. with respect to different concrete samples made with different mix proportions. Furthermore, a prediction model is introduced to predict compressive and splitting tensile strength using the machine learning tool support vector machine (SVM). A data set containing 108 records either for compressive or tensile strength was used for the training and testing purposes of the SVM model. A total of 9 mix proportions was selected and cast cylinders were cured for 7, 28, and 56 days. Four different kernel functions were used to optimize the results and different accuracy parameters like the value of R2, mean absolute error, mean square error, root mean square error, etc. were compared to find the best kernel function for this study. By primarily evaluating the coefficient of determination (R2), SVM showed an acceptable result with an accuracy of over 90%. Moreover, in terms of other accuracy measurement parameters result indicates that the SVM is an effective tool to predict the compressive and splitting tensile strength of concrete comprised of different proportions of ceramic content. [ABSTRACT FROM AUTHOR]

AbstractThe study proposes a novel concept of a curved single lap joint (SLJ) with non- uniform adhesive thickness to reduce stress concentrations near overlap edges. Experimental and numerical methodologies investigate this joint using two adhesives: Araldite 2015-1 and AV138. Numerical models in ABAQUS predict cohesive failure in the adhesive layer. The curved configuration doesn’t significantly enhance strength in SLJ using ductile adhesives, but for brittle ones, strength increased by 131% due to sensitivity to peak stresses. In conclusion, the curved SLJ effectively reduces stress concentrations and the effectiveness of this concept is linked to the ductility of the adhesive used. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The industry has advanced in replacing traditional joining methods, such as screwing/riveting and welding, especially the aeronautical and automotive industries, which have adopted the adhesive bonding method. This method has the advantages of simplifying the process, improving performance in terms of fatigue load, and improving the union between dissimilar materials. In addition, the development of computational numerical methods, with greater precision, has contributed to the dimensioning of joints, as well as the prediction of the resistance of these joints, which has contributed to more reliable simulations of different adhesive bonding solutions and their industrial implementation. Different modifications to the conventional designs are addressed in the literature to achieve the best results, including geometrical and material modifications. In the present research, the strength of single-step single-adhesive joints and bi-adhesive joints (BAJ) manufactured with AW 6082-T651 aluminium adherends is experimentally and numerically studied for different overlap lengths (LO). Three commercial adhesives were studied, from brittle to ductile, along with different adhesive combinations in the BAJ technique. The experimental work was mainly used for numerical model validation. The numerical analysis took advantage of triangular cohesive zone models (CZM) and included the study of peel and shear stresses, strength, and energy to failure. It was possible to validate the CZM accuracy by comparison with the experimental data. The analysis carried out showed that the BAJ technique did not reveal significant increases in strength compared to single-adhesive joints. However, when damage tolerance and dissipated energy are analysed, a noticeable increase in performance is observed, especially in joints with larger LO.Article highlights: Experimental and cohesive zone model parametric study of step adhesive joints with bi-adhesive concept; The bi-adhesive joint provided strength and energy absorption advantages over single-adhesive joints; Design guidelines are provided regarding the geometry and adhesive combination to provide best performance. [ABSTRACT FROM AUTHOR]

The article examines and assesses the phenomenological strength theory of composite materials. A comparative analysis of the theoretical envelopes was conducted for each criterion. A unified form of the phenomenological strength criterion was established. The study specifically examined the effects of altering the interaction parameter on the Tsai-Wu criterion’s theoretical envelope. Based on the available experimental data, the study plotted the failure envelopes of each strength criterion under planar composite stress states. The variation of these envelopes across various stress quadrants was highlighted. As a result of the examinations, four typical phenomenological strength criteria were chosen. The composites’ off-axis tensile and biaxial loading test data were used to evaluate the predictive power objectively. The results showed that not all stress states’ test results agreed with the predictions of the phenomenological strength theory. The criterion proposed by Norris and Tsai-Hill performed better at accounting for the material’s different compressive and tensile characteristics. The other criteria tended to be conservative under particular circumstances. Simultaneously, the Hoffman criterion matched the test data more closely over a broader range of stress states. Overall, this study clarified the limitations and applicability of various strength criteria in composite material strength prediction.

Abstract The industry has advanced in replacing traditional joining methods, such as screwing/riveting and welding, especially the aeronautical and automotive industries, which have adopted the adhesive bonding method. This method has the advantages of simplifying the process, improving performance in terms of fatigue load, and improving the union between dissimilar materials. In addition, the development of computational numerical methods, with greater precision, has contributed to the dimensioning of joints, as well as the prediction of the resistance of these joints, which has contributed to more reliable simulations of different adhesive bonding solutions and their industrial implementation. Different modifications to the conventional designs are addressed in the literature to achieve the best results, including geometrical and material modifications. In the present research, the strength of single-step single-adhesive joints and bi-adhesive joints (BAJ) manufactured with AW 6082-T651 aluminium adherends is experimentally and numerically studied for different overlap lengths (L O). Three commercial adhesives were studied, from brittle to ductile, along with different adhesive combinations in the BAJ technique. The experimental work was mainly used for numerical model validation. The numerical analysis took advantage of triangular cohesive zone models (CZM) and included the study of peel and shear stresses, strength, and energy to failure. It was possible to validate the CZM accuracy by comparison with the experimental data. The analysis carried out showed that the BAJ technique did not reveal significant increases in strength compared to single-adhesive joints. However, when damage tolerance and dissipated energy are analysed, a noticeable increase in performance is observed, especially in joints with larger L O.

The strength prediction of pervious concrete is hard to implement for the mix design due to the porous structure. This work studied the influence of the water-to-cement ratio on the fluidity, viscosity, and mechanical properties of cement paste. Then, the porosity, permeability, and compressive strength of the pervious concrete with various porosities were investigated, and the test results were fitted and analyzed. The result indicates that as the water-to-cement ratio increases, the viscosity of the cement paste reduces and the fluidity increases. The water-to-cement ratio has a negative linear relationship with net slurry strength. The porosity and permeability of pervious concrete fluctuate in accordance with the same rule as the water-to-cement ratio changes. The compressive strength of pervious concrete with varying design porosities increases initially, then declines as the water-to-cement ratio rises. According to the linear fitting analysis, when the water-to-cement ratio is constant, the permeability and compressive strength of pervious concrete have a positive and negative linear relationship with the design porosity, respectively. By analyzing the fitting results and combining the volume method of pervious concrete, a calculation method for mix proportion design is proposed to predict the strength of pervious concrete.

Concrete in cold environments may suffer from frost damage, which effects the hydration reaction and early strength development of cement. This study aims to test the early strength development of recycled coarse aggregate concrete in cold region using an assisted heating method. Based on Nurse-Saul and Hansen-Pedersen maturity calculation methods, the early strength of recycled and natural coarse aggregate concrete is fitted and predicted using exponential and logarithmic type functions. The results showed that active heating and thermal insulation can ensure early concrete hydration. The temperature of recycled coarse aggregate concrete was slightly higher than that of natural concrete, while its strength was slightly lower. The early strength development of natural and recycled coarse aggregate concrete correlated more with the logarithmic type function. The prediction error of natural and recycled coarse aggregate concrete is basically within 10% for the first 14 days of compressive strength.

In order to study the relationship between the compressive strength and pore structure of carbonized concrete, normal concrete (NC), basalt-polypropylene hybrid fiber-reinforced concrete (BPC), and steel-polypropylene hybrid fiber-reinforced concrete (SPC) were prepared. The compressive strength and pore structure of concrete with carbonation ages of 0, 7, 14, 28, and 56 days were tested, and the empirical formulas of total air content–compressive strength were obtained. The results show that the compressive strength can be well predicted from the total air content of concrete through all the empirical formulas of linear, logarithmic, exponential, and power functions. The formulas of air content in specific pore size range and compressive strength of concrete were obtained. The air content of both small and medium pores has a strong negative linear correlation with compressive strength. The applicability of the two formulas is good. Most of the theoretical and measured values deviate within ±15%. The air content–compressive strength model based on specific pore size range is more accurate than the total air content–compressive strength model. By summarizing a large number of literature data and analyzing the measured data, this study obtained widely applicable compressive strength–air content relationship formulas, which is of great significance to the compressive strength prediction of normal concrete and hybrid fiber-reinforced concrete. [ABSTRACT FROM AUTHOR]

This paper reports on the compressive properties of a new steel wire– and carbon/glass hybrid fiber–reinforced polymer (S-CG HFRP) bar. The influence of the carbon and glass fiber volume proportion (Vc/Vg), steel wire replacement ratio (Vs), fiber volume ratio (Vf), fiber arrangement, and ratio of the unbraced length to the bar diameter (Lu/d) on the compressive behavior of the S-CG HFRP bars was experimentally investigated. The failure mechanisms were analyzed, revealing that the failure mode changed from fiber microbuckling to fiber macrobuckling with an increase in Lu/d. Adding steel wires resulted in a decrease in the compressive modulus of the S-CG HFRP bars. The compressive strength of the S-CG HFRP bars was not effectively improved by adding steel wires or carbon fibers and was governed by the Lu/d ratio. The results showed that the compressive modulus and strength of the S-CG HFRP bars were approximately 90% and 28% of their tensile modulus and strength, respectively. Finally, the relationship between the compressive and tensile properties of the S-CG HFRP bars was established. Simplified empirical equations were proposed to estimate the compressive strengths of S-CG HFRP bars with different Lu/d ratios. [ABSTRACT FROM AUTHOR]

In the last years, aeronautical industry has developed certified composite patch bonded repairs for damage tolerant structures, but only restoring metallic components. The mechanical behaviour of composite bonded repairs has been investigated since the development of composite damage tolerant structures is directly dependent on this technology. This study presents a strength prediction on carbon-epoxy adhesively repaired component, using ARALDITE® 2015 for the repairing and recurring to ABAQUS®, applying cohesive zone modelling (CZM), four different load conditions: tension, compression, upward bending and downward bending were tested. Four types of repairs are analysed: Single-Strap, Double-Strap, Scarf and Two-Stepped. The obtained results state that the Double-Strap repair withstand higher loads considering all the applied load cases when compared with the other geometries. Scarf and Two-Stepped repairs could achieve promising behaviours and they should be optimized, considering the advantages related to embedded patch repairing. Generally, the overlap length revealed to favour higher strengths, specially increasing from 5 to 10 mm. Failure paths were demonstrated, revealing proper relations with strength predictions. [ABSTRACT FROM AUTHOR]

Viscoelastic models are typically employed to investigate the mechanical properties of yarn. In this study, a viscoelastic model was developed to predict the tensile stress–strain relationship of a core/wrapped composite yarn with double filaments. The tensile properties of the yarn were tested, and various stages of the tensile curve were analyzed. Moreover, based on the tensile fracture characteristics, a five-element nonlinear viscoelastic model comprising Kelvin element, Maxwell element, and linear springs was established. Furthermore, the tensile properties of the composite yarns were simulated and calculated using the developed model. Additionally, the stress–strain relationship was fitted using a polynomial on the basis of the established model. The results reveal that the tensile fracture curve of the composite yarn comprises three stages. They are a small strain linear stage, a large strain stage, and a strength fluctuation stage. The viscoelastic tensile model can decently explain the three-stage stress–strain characteristics of the composite yarn tensile curve. The theoretical results were consistent with the experimental results, and the correlation coefficient was greater than 0.999. Based on the results, the proposed model can be employed with high accuracy to predict the tensile properties of a core/wrapped composite yarn with a double filament. [ABSTRACT FROM AUTHOR]

This paper presents the results of an experimental study on saturated silty clay–concrete interface shear behavior, and a shear strength prediction model is established for the silty clay–concrete interface. Based on three-dimensional (3D) scanning data of actual concrete pile surfaces, regular and irregular concrete surfaces with different roughnesses are replicated in the laboratory tests. The interface shear strength is found to increase as the roughness coefficient of concrete increases; the shear process of a regular interface shows a strain softening feature, while that for an irregular interface shows strain hardening. The effect of a normal stress increase on the residual strength of a regular interface is more obvious. The modified Grasselli roughness evaluation method is reasonably effective in characterizing both regular and irregular contact surfaces. In the framework of this method, a prediction formula for the estimation of peak strength of a clay–concrete interface is proposed, which performs favorably well in comparison with test results obtained from this and previous studies. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Self-compacting mortar (SCM) has superior workability and long-term durable performance compared to traditional mortar. The strength of SCM, including both its compressive and flexural strengths, is a crucial property that is determined by appropriate curing conditions and mix design parameters. In the context of materials science, predicting the strength of SCM is challenging because of multiple influencing factors. This study employed machine learning techniques to establish SCM strength prediction models. Based on ten different input parameters, the strength of SCM specimens were predicted using two different types of hybrid machine learning (HML) models, namely Extreme Gradient Boosting (XGBoost) and the Random Forest (RF) algorithm. HML models were trained and tested by experimental data from 320 test specimens. In addition, the Bayesian optimization method was utilized to fine tune the hyperparameters of the employed algorithms, and cross-validation was employed to partition the database into multiple folds for a more thorough exploration of the hyperparameter space while providing a more accurate assessment of the model's predictive power. The results show that both HML models can successfully predict the SCM strength values with high accuracy, and the Bo-XGB model demonstrated higher accuracy (R2 = 0.96 for training and R2 = 0.91 for testing phases) for predicting flexural strength with low error. In terms of compressive strength prediction, the employed BO-RF model performed very well, with R2 = 0.96 for train and R2 = 0.88 testing stages with minor errors. Moreover, the SHAP algorithm, permutation importance and leave-one-out importance score were used for sensitivity analysis to explain the prediction process and interpret the governing input variable parameters of the proposed HML models. Finally, the outcomes of this study might be applied to guide the future mix design of SCM specimens. [ABSTRACT FROM AUTHOR]

The utilization of solid waste for filling mining presents substantial economic and environmental advantages, making it the primary focus of current filling mining technology development. To enhance the mechanical properties of superfine tailings cemented paste backfill (SCPB), this study conducted response surface methodology experiments to investigate the impact of various factors on the strength of SCPB, including the composite cementitious material, consisting of cement and slag powder, and the tailings' grain size. Additionally, various microanalysis techniques were used to investigate the microstructure of SCPB and the development mechanisms of its hydration products. Furthermore, machine learning was utilized to predict the strength of SCPB under multi-factor effects. The findings reveal that the combined effect of slag powder dosage and slurry mass fraction has the most significant influence on strength, while the coupling effect of slurry mass fraction and underflow productivity has the lowest impact on strength. Moreover, SCPB with 20% slag powder has the highest amount of hydration products and the most complete structure. When compared to other commonly used prediction models, the long-short term memory neural network (LSTM) constructed in this study had the highest prediction accuracy for SCPB strength under multi-factor conditions, with root mean square error (RMSE), correlation coefficient (R), and variance account for (VAF) reaching 0.1396, 0.9131, and 81.8747, respectively. By optimizing the LSTM using the sparrow search algorithm (SSA), the RMSE, R, and VAF improved by 88.6%, 9.4%, and 21.9%, respectively. The research results can provide guidance for the efficient filling of superfine tailings. [ABSTRACT FROM AUTHOR]

This paper focuses on detailed experimental and numerical investigations carried out to study the behaviour of reinforced recycled aggregate concrete (RAC) short columns subjected to uniaxial compression. Experimental investigation includes the study of the behaviour of short reinforced RAC columns in which 30% of the natural coarse aggregates were replaced with recycled coarse aggregates and percentage of longitudinal steel reinforcement was also varied. Maximum axial load and failure modes of columns were observed. Numerical investigations were also carried out to simulate the behaviour of short RAC columns that were tested during the study along with some other columns of same size tested in another study by the authors. Software used in the simulation was first tested by modelling and simulating behaviour of columns obtained from past researches. Results were compared in terms of ultimate load carrying capacities and failure patterns. Results obtained experimentally were found to be in close agreement when compared with the results predicted through simulation. Load carrying capacities of the tested columns were also compared with equations proposed by different design codes. Lot of variation was observed in the peak loads predicted by the equations proposed by different design codes as they have been proposed for natural aggregate concrete (NAC) columns and a correction factor needs to be introduced depending on the physical properties of RCA if they are to be used for RAC. [ABSTRACT FROM AUTHOR]

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Glass as a construction material has become indispensable and is still on the rise in the building industry. However, there is still a need for numerical models that can predict the strength of structural glass in different configurations. The complexity lies in the failure of glass elements largely driven by pre-existing microscopic surface flaws. These flaws are present over the entire glass surface, and the properties of each flaw vary. Therefore, the fracture strength of glass is described by a probability function and will depend on the size of the panels, the loading conditions and the flaw size distribution. This paper extends the strength prediction model of Osnes et al. with the model selection by the Akaike information criterion. This allows us to determine the most appropriate probability density function describing the glass panel strength. The analyses indicate that the most appropriate model is mainly affected by the number of flaws subjected to the maximum tensile stresses. When many flaws are loaded, the strength is better described by a normal or Weibull distribution. When few flaws are loaded, the distribution tends more towards a Gumbel distribution. A parameter study is performed to examine the most important and influencing parameters in the strength prediction model. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The strength of the backfill body is an important aspect of safe production in backfill mining technology. In order to quickly and accurately obtain the strength of coal gasification slag -based cemented backfill materials with different ratios, the orthogonal design of 4 factors and 5 levels and variance analysis method were used to study the mass concentration (A), coal gasification slag (CGS)/cement (B), bone glue ratio (C), activator content (D) on the compressive strength of backfill body and a genetic algorithm with 28-day compressive strength of CGS-based backfill material as the response value was constructed, and verify the applicability of the model. The results show that: the primary and secondary factors affecting the 28-day compressive strength are C > A > B > D, the optimal ratio is 80% mass concentration, CGS/cement ratio 4:1, bone glue ratio 3:7, and activator 3%, according to orthogonal variance analysis; the activity of CGS is gradually stimulated as the curing age grows, the hydration products Ettringite and C-S-H are continually generated, and the compressive strength of the backfill material increases in tandem; In the GA-ELM model test set, the intensity correlation coefficient between the prediction and the measured value reached an average of 0.99, and the prediction accuracy was 71.8% and 43.5% higher than that of ELM and PSO-ELM respectively; Laboratory studies have shown that the GA-ELM prediction model has a prediction accuracy of 98.2% and that it can accurately estimate the strength of CGS cemented backfill. [ABSTRACT FROM AUTHOR]

Promoting the use of agricultural wastes/byproducts in concrete production can significantly reduce environmental effects and contribute to sustainable development. Several experimental investigations on such concrete's compressive strength ( f c ) and behavior have been done. The results of 229 concrete samples made by oil palm shell ( OPS ) as a lightweight aggregate ( LWA ) were used to develop models for predicting the f c of the high-strength lightweight aggregate concrete ( H S - L W A C ). To this end, gene expression programming ( GEP ), adaptive neuro-fuzzy inference system ( ANFIS ), artificial neural network ( ANN ), and multiple linear regression ( MLR ) are employed as machine learning ( ML ) and regression methods. The water-to-binder ( W / B ) ratio, ordinary Portland cement ( OPC ), fly ash ( FA ), silica fume ( SF ), fine aggregate ( Sand ), natural coarse aggregate ( Gravel ), OPS , superplasticizer ( SP ) contents, and specimen age are among the nine input parameters used in the developed models. The results show that all ML -based models efficiently predict the H S - L W A C 's f c , which comprised OPS agricultural wastes. According to the results, the ANN model outperformed the GEP and ANFIS models. Moreover, an uncertainty analysis through the Monte Carlo simulation (MCS) method was applied to the prediction results. The growing demand for sustainable development and the crucial role of eco-friendly concrete in the construction industry can pave the way for further application of the developed models due to their superior robustness and high accuracy in future codes of practice. [ABSTRACT FROM AUTHOR]