Shrinkage Prediction Models Research Articles

Mechanical and autogenous shrinkage properties of coarse aggregate ultra-high performance concrete (CA-UHPC): The effect of mineral admixtures

To reduce the shrinkage and explore the best content of mineral admixtures of coarse aggregate ultra-high performance concrete (CA-UHPC), this study conducted 12 groups of autogenous shrinkage, basic material properties (compressive strength, tensile strength, and flowability), and X-ray diffraction experiments on CA-UHPC with coarse aggregate and different amounts of mineral admixtures (slag powder, limestone powder). The effects of coarse aggregate and mineral admixtures on the autogenous shrinkage and basic material properties of CA-UHPC were analyzed, and the measured values of CA-UHPC autogenous shrinkage strain were compared with the theoretical values calculated by six commonly used concrete shrinkage prediction models. The experimental results show that under the condition of 15 % coarse aggregate content and no steam curing, a CA-UHPC with a compressive strength greater than 110 MPa, a tensile strength greater than 11 MPa, a flowability greater than 290 mm, and an autogenous shrinkage of less than 700 after 120d can be obtained. The mineral admixture has a significant impact on the fundamental properties of CA-UHPC. The compressive strength, tensile strength, and flowability of CA-UHPC with 15 % limestone powder and 15 % S95 slag powder increased by 6.6 %, 5.4 %, and 27.6 % compared to UHPC with only coarse aggregate added, and the autogenous shrinkage after 120 days decreased by 10.5 %. However, when the dosage of limestone powder and S95 slag powder exceeds 30 %, the tensile and compressive strength of CA-UHPC will rapidly decrease. This study also revised the GL 2000 shrinkage prediction model based on the CA-UHPC autogenous shrinkage test results, introduced the influence coefficient of mineral admixture content on autogenous shrinkage strain, and established a theoretical calculation model suitable for CA-UHPC autogenous shrinkage strain.

Autogenous shrinkage prediction models and microstructure of UHPC with single or binary addition of an expansive agent and steel fibers

The low water/binder ratio of ultra-high performance concrete (UHPC) often results in its high autogenous shrinkage. Our study explored the effect of the single or binary addition of a CaO-based expansive agent (CEA) and steel fibers on flowability, compressive strength, flexural strength, microstructure, and autogenous shrinkage of UHPC. X-ray diffraction (XRD), thermogravimetric (TG) analysis, scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) were applied to reveal the effects of CEA and steel fibers on hydration products and microstructure characteristics of UHPC. Experimental results show that the autogenous shrinkage of UHPC decreased markedly with the single or binary addition of CEA and steel fibers. Relative to the control group, autogenous shrinkage of UHPC with 2.5% dosage of single steel fibers, 6% dosage of single CEA, and binary addition of 2.5% steel fibers and 6% CEA decreased 17.8%, 10.9%, and 30.8% at 180 days, respectively. Steel fibers could enhance the mechanical performance of UHPC; nevertheless, they would decrease the flowability of UHPC. Meanwhile, the addition of CEA in the UHPC mixture not only maintained the mechanical properties and flowability but also decreased the autogenous shrinkage. Diffraction peak intensity and endothermic peak of Ca(OH)2 and the pore volume of 10–50 nm diminished with the content of CEA; however, that of C-S-H gel and ettringite increased. The prediction accuracy of nine shrinkage models (FHWA model, Lee model, Yoo model, JSCE model, B4 model, JonassonH model, Eurocode 2 model, CEB model, and DilgerW model) is analyzed with RE, Rnew2, and autogenous shrinkage of UHPC in this paper.

Autogenous shrinkage of high-performance concrete subjected to different curing temperatures

High-performance concrete (HPC) shows obvious autogenous shrinkage in the early stage. In actual engineering, the interior of HPC undergoes a variable temperature evolution, and the impact of variable temperature conditions on the autogenous shrinkage of HPC is unclear. This paper examines the impact of different curing temperatures on the material properties of HPC, as well as the evolution of HPC autogenous shrinkage. Three measured temperature histories and four constant temperatures were used as curing conditions. Additionally, an HPC autogenous shrinkage prediction model was established considering the impact of curing temperature. The test results indicate that the compressive strength and elastic modulus of HPC increase with increasing curing temperature. Furthermore, there is a significant increase in the autogenous shrinkage rate of HPC as the curing temperature increases. Existing maturity methods overestimate the impact of temperature on the autogenous shrinkage process of HPC. The proposed autogenous shrinkage prediction model enhances the accuracy in predicting the autogenous shrinkage of HPC under constant and variable curing temperature conditions.

The influence of accelerator type on the volume stability of shotcrete and the establishment of shrinkage model

The characteristics of shotcrete can lead to significant shrinkage deformation, presenting potential threats to tunnel structure safety. To research this concern, this study investigated the effects of three accelerators on the volume stability of cement paste, cement mortar, and concrete. The correlation equations of shrinkage deformation of cement paste, cement mortar and concrete were established, and the drying shrinkage prediction model of cement mortar with accelerator was constructed by combining the ACI-209 model and the GL-2000 model. Findings from this study show that the AR accelerator had the greatest autogenous shrinkage and drying shrinkage, with the highest mass loss under drying conditions. The study demonstrated that there exists a good linear relationship between the shrinkage deformation of cement paste, cement mortar, and concrete with accelerator. Finally, the proposed prediction model for drying shrinkage of cement mortar exhibits strong predictive capabilities.

Analysis and Prediction Model Reinforced UHPC Shrinkage Property

This paper explores the shrinkage of reinforced UHPC under high-temperature steam curing and natural curing conditions. The results are compared with the existing shrinkage prediction models. The results show that the maximum shrinkage strain of reinforced UHPC after steam curing is 164 με and gradually becomes zero. As for natural curing, the maximum shrinkage strain is 173 με and the value stabilizes on the 10th day after pouring. This indicated that steam curing can significantly reduce shrinkage time. Compared with the plain UHPC tested in the previous literature, the structural reinforcement can significantly inhibit the UHPC shrinkage and greatly reduce the risk of cracking due to shrinkage. By comparing the results in this paper with the existing models for predicting the shrinkage strain development, it is found that the formula recommended in the French UHPC structural and technical specification is suitable for the shrinkage curve in the present paper.

Enhancing and comparing shrinkage prediction models for High-Strength Concrete with and without admixtures

This study aimed to improve and compare the parameterization of three prominent shrinkage prediction models—RILEM B4, MC 2010, and WITS—tailored specifically for High-Strength Concrete (HSC), both with and without the inclusion of admixtures. The dataset used for refining model parameters consisted of 220 experiments related to drying shrinkage and 342 experiments concerning autogenous shrinkage. Model performance evaluation involved various statistical metrics applied to the entire HSC dataset, subdatasets, and distinct time periods of shrinkage (0–99 days, 100–199 days, 200–499 days, and ≥500 days). The statistical indicators included Root Mean Square Error (RMSE), R-squared adjusted (R2 adj), Akaike’s Information Criterion (AIC), and the overall coefficient of variation (C.o.Vall). Modified models exhibited significantly improved predictions compared to the original models, with most predictions falling within ±20% of the measured shrinkages. For HSC drying shrinkage, the original model accuracy ranked as WITS, RILEM B4, and MC 2010. However, after parameter adjustments, WITS, MC 2010, and RILEM B4 were the best-performing models. Conversely, for HSC autogenous shrinkage predictions, the RILEM B4 model surpassed the MC 2010 model, demonstrating superior accuracy and reliability in forecasting this specific type of shrinkage behaviour within High-Strength Concrete.

The data-driven research on the autogenous shrinkage of ultra-high performance concrete (UHPC) based on machine learning

This paper performs the data-driven research on the autogenous shrinkage of Ultra-High Performance Concrete (UHPC) based on multiple machine learning algorithms. The autogenous shrinkage prediction model of UHPC with high prediction accuracy (R2 = 0.89) and strong generalization capability is established based on Gradient Boosting (GB) algorithm. The Graphic User Interface (GUI) of UHPC autogenous shrinkage prediction is designed, which assists in scientific research and engineering applications. Cement, silica fume, water, superabsorbent polymer, and expansive agent contents that are closely related to hydration process have prominent impacts on autogenous shrinkage of UHPC, while the fly ash, steel fibers, and sand contents show the slight impacts. The additions of superabsorbent polymer, expansive agent, steel fibers, sand, and superplasticizer effectively reduce the autogenous shrinkage of UHPC. Lower water to cement ratio causes larger autogenous shrinkage of UHPC.

A numerical model for predicting the drying shrinkage behavior of concrete under ultra-low relative humidity

The drying shrinkage caused by rapid water loss will pose a great threat to the safety of concrete structures, especially for those serviced in an extremely dry environment. However, the majority of reported research primarily focuses on drying shrinkage behavior of concrete under relative humidity (RH) ranging from 50% to 100%, and the corresponding drying shrinkage prediction models have poor applicability, leading to an unclear understanding of it under ultra-low RH (RH<40%). In this study, a numerical model, finite element model with cohesive elements, was proposed to better predict the drying shrinkage behavior of concrete, even under the RH of 20%. Initially, geometric models of concrete with varying coarse aggregate size ranges and volume fractions were established. Subsequently, based on the relationship between RH and humidity diffusion coefficient of concrete with different water-cement ratio (w/c), a drying shrinkage model was proposed to examine the corresponding shrinkage strain of concrete even under ultra-low RH conditions, and its validity was verified through comparison with reported experimental research. Utilizing the developed model, the effects of size range and volume fraction of coarse aggregate, and the w/c, on the drying shrinkage of concrete across a wide range of RH levels were systematically investigated. The results indicated that increasing the aggregate size range can only slightly inhibit drying shrinkage, while there is a marked inhibition effect on it (decrease by about 45% at most) for increasing the aggregate vol% or reducing w/c, even under ultra-low RH of 20%. Finally, grey relation analysis was employed to assess the inhibition weight of each parameter on the drying shrinkage, following this sequence in order: RH > w/c > coarse aggregate vol% > coarse aggregate range size. We also envision that the proposed model could provide feasible guidelines for the inhibition of concrete in ultra-dry regions.

Effects and mechanisms of component ratio and cross-scale fibers on drying shrinkage of geopolymer mortar

This study initially examined the impact of varying component ratios on the drying shrinkage of geopolymer mortars. It further explored the drying shrinkage inhibition of cross-scale hybrid fibers, specifically carbon nanotubes (CNTs) and hooked steel fibers (HSFs). The findings indicate that increasing the content of granulated blast furnace slag (GBFS) or reducing the water-binder ratio and binder-sand ratio could effectively minimize the shrinkage of geopolymer mortar. The addition of fibers also had an inhibitory effect on shrinkage. Hybrid fibers exhibited better performance compared to steel fibers, and steel fibers performed better than CNTs. Finally, the mechanical mechanisms of fibers and aggregates on the shrinkage inhibition of geopolymer mortars was revealed and the drying shrinkage prediction models that suitable for fiber reinforced geopolymer mortars was proposed.

Shrinkage prediction model of high strength lightweight aggregate concrete based on relative humidity

Concrete shrinkage affects the overall stability and long-term service performance of the structures. Lightweight aggregates (LWAs) with a porous structure are an ideal internal curing material to mitigate concrete shrinkage. Nevertheless, the low elastic modulus of LWAs has a negative impact on concrete shrinkage. In order to clarify the influences of LWAs in the high strength lightweight aggregate concrete (HSLC) on its shrinkage performance, the compressive strength, internal relative humidity (IRH), elastic modulus, and autogenous shrinkage (AS) of HSLC were analyzed experimentally in this study. The correlation of these parameters was investigated. As indicated by the results, compressive strength and elastic modulus were negatively related to the content of LWAs. The 28-day AS and IRH of HSLC and the critical moment of IRH increased by 3.9%, 20.2% and 36 h, respectively, when the content of LWAs increased from 0% to 100%. A linear relationship between AS and compressive strength and an exponential relationship between AS and IRH were established. Additionally, a shrinkage prediction model considering IRH of concrete and water absorption by LWAs was proposed for HSLC.

Prediction model and compensation method for curing shrinkage of inkjet 3D printing parts

Inkjet 3D printing has huge potential for applications in electronics printing, ceramic printing, and other fields, primarily due to the excellent dimensional accuracy of printed products, which is particularly important for device performance. However, in inkjet 3D printing, the fusion and solidification of droplets can cause the samples to shrink in various directions, resulting in a deviation from the designed target size, and subsequently affecting the performance of the part. In this study, the curing shrinkage behavior of parts in inkjet 3D printing is examined. First, a shrinkage model of the droplet element is established using the finite element method in accordance with the material parameters, and the linear shrinkage rates of different droplet groups are obtained. A curing shrinkage prediction model of the printed parts is then established, and the size and shape of the cured parts are predicted accordingly. A height calculation model of the printed parts is established according to the concept of volume conservation, and the height prediction of the printed parts is realized according to the number of printed layers and their resolution. An approach for compensation of the print model graphic layers is developed using the established prediction model. Experiments using two printing materials with different degrees of shrinkage are conducted to verify the feasibility of this method. The results indicate that the proposed curing shrinkage prediction model can seamlessly capture the curing shrinkage behavior of printed parts for materials with different shrinkages. Furthermore, after shrinkage, the model-based print compensation method achieves the measured sizes that are closer to the target size than that without compensation. The dimensions of inkjet 3D printed parts can be accurately controlled using this method.

Development of creep and shrinkage prediction models for recycled aggregate concrete

AbstractStudies have found utilization of recycled aggregate concrete (RAC) for engineering use to be environmentally favorable by preserving natural resources and limiting waste reduction. However, the use of RAC has not been effectively integrated in the industry due to limited research overlying the critical areas of its creep and shrinkage behavior. Hence, this study aims to investigate the natural aggregate concrete creep and shrinkage prediction models and perform recycled concrete aggregate (RCA) replacement level and water absorption modifications to illustrate the presence and behavior of RAC. Comparative analysis was undertaken by establishing comprehensive conventional concrete and RAC experimental databases and investigating several creep and shrinkage prediction models including, ACI 209R‐92, Bazant–Baweja B3, CEB MC 90, GL 2000, MC 2010, and JSCE 2010. The Bazant–Baweja B3 model was found to present the most optimal results by evaluating each conventional model against the RAC databases and performing regression analysis. Hence, the Bazant–Baweja B3 natural aggregate concrete models for creep and shrinkage were then effectively modified to illustrate the presence of RCA replacement level and water absorption. The adjusted models were verified with a set of experimental databases which successfully presented favourable results with coefficient of correlation factors increased from an average of 0.76 to 0.9.

Development of autogenous shrinkage prediction model of alkali-activated slag-fly ash geopolymer based on machine learning

This paper developed an autogenous shrinkage prediction tool with high accuracy through machine learning for alkali-activated slag-fly ash geopolymer. The influencing factors of autogenous shrinkage of activated slag-fly ash geopolymer paste and mortar were analyzed. The results show that Extreme Gradient Boosting (XGB) algorithm achieves the best prediction performance with R2 of over 0.90 and strong generalization ability for predicting the autogenous shrinkage of alkali-activated slag-fly ash geopolymer paste and mortar. The decrease in W/B, alkali dosage and slag content can reduce the autogenous shrinkage of alkali-activated slag-fly ash geopolymer paste, while increasing W/B and decreasing alkali dosage are beneficial to mitigate the autogenous shrinkage of alkali-activated slag-fly ash geopolymer mortar. The Graphical User Interface (GUI) used for autogenous shrinkage prediction of alkali-activated slag-fly ash geopolymer paste or mortar was designed, which can directly be used for predicting autogenous shrinkage on the premise of knowing the synthesis parameters of geopolymer. This prediction tool can prejudge the autogenous shrinkage of alkali-activated slag-fly ash geopolymer materials instead of preforming cumbersome autogenous shrinkage test, which will significantly reduce the workload if we only need to know the autogenous shrinkage.

Study on the shrinkage mechanism and prediction model of high-strength specified density concrete based on the internal curing micro-pump effect of MSW incineration tailings

As an effective substitute for high-strength concrete, high-strength specified density concrete can prevent and reduce the autogenous shrinkage cracking of high-strength concrete caused by low water-binder ratio or dry environment. In this paper, the early-stage shrinkage law of high-strength specified density concrete prepared from the lightweight aggregate of municipal solid waste (MSW) incineration tailings was explored through autogenous shrinkage and non-contact dry environment shrinkage tests. Combined with the tests of relative humidity, capillary negative pressure, penetration resistance, mesoporous structure and hydration products, the shrinkage reduction mechanism of internal curing micro-pump effect of incineration tailings in high-strength specified density concrete was revealed systematically and comprehensively. What’s more, the shrinkage prediction model of high-strength specified density concrete was established accordingly. The results show that replacing high-strength concrete with high-strength specified density concrete can effectively reduce the autogenous-shrinkage value by more than 68.5%, and incineration tailings can be widely used as internal curing material in cement concrete. It deserves to be mentioned that incineration tailings have significant internal curing micro-pump effect in high-strength specified density concrete. More importantly, the micro-pump water releases effect before the critical humidity point (RH = 100%) ameliorates the hydration degree of high-strength specified density concrete (SDC-25, SDC-50 and SDC-75) by 34.9%, 69.9% and 103.4%, and the self-expansion effect produced compensates for the volume shrinkage resulted from chemical shrinkage. Nevertheless, after the critical humidity point (RH < 100%), the water release effect of micro-pump lessens the pore negative pressure. The difference between the critical point test results of SDC-25, SDC-50 and SDC-75 relative humidity and the modified predicted shrinkage values of Zhang Jun-Hou Dongwei is 2.4%, 1.8% and 1.3%, respectively. The deviation between the two is within 20%, and the correlation coefficient (R2) is 0.943, which suggests that the modified Zhang Jun-Hou Dongwei model is applicable to the shrinkage prediction of high-strength specified density concrete of the shrinkage reduction type of internal curing. By adjusting the aggregate composition structure of traditional high-strength concrete and introducing internal curing micro-pump effect, it is helpful to improve the shrinkage resistance of high-strength concrete and the technical progress of the resource utilization of bulk solid wastes (incineration tailings) in cement concrete.

Mortar film thickness on the autogenous shrinkage of concrete: Test and simulation

Coarse aggregate (CA) accounts for the largest volume in concrete, which has significant impact on the performance of this building material such as workability, strength and shrinkage. Based on the determination of specific surface area for CA through simple sieving curve, mortar film thickness (MFT) of fresh concrete is numerically calculated. Relationship between MFT and CA content is acquired as three-stage model with critical volume fractions of 17% and 67%. Autogenous shrinkage of concrete with various CA volume fractions were experimentally evaluated through integrated shrinkage test devices. Zero-time of autogenous shrinkage is determined based on the expansion-shrinkage behavior of concrete mixtures at very early age. The ultimate autogenous shrinkage of concrete decreases monotonously with increasing amount of CA or decrease in MFT. By comparing to existing theoretical and empirical models, new prediction model of autogenous shrinkage based on MFT is proposed with high correlation coefficient for concrete containing various CA content.

Shrinkage Prediction Model of High Strength Lightweight Aggregate Concrete Based on Relative Humidity

Shrinkage Prediction Model of High Strength Lightweight Aggregate Concrete Based on Relative Humidity

Effect of temperature rise inhibitor on early-age behavior and cracking resistance of high strength concrete under uniaxial restrained condition

Temperature rise inhibitor (TRI) is used to modify the cement hydration and reduce the heat release of high strength concrete (HSC). Many investigations on the mechanical properties, temperature process, and autogenous shrinkage of cement-based materials with TRI have been conducted. However, the evaluation of the early-age behavior and cracking resistance of HSC with TRI under uniaxial restrained condition considering the effects of temperature, shrinkage, restrained stress, and tensile creep simultaneously was rarely conducted. Temperature stress test machine, which could measure these factors simultaneously, was used to investigate the effect of TRI contents on the early-age behavior and cracking resistance of HSC in this research. The peak temperature, temperature rising rate, autogenous shrinkage, and tensile creep decreased with increasing TRI contents. The addition of TRI improved the early-age cracking resistance of HSC. A modified stress-strain failure criterion and a prediction model for autogenous shrinkage of HSC with TRI were proposed.

A Creep and Shrinkage Prediction Model for Serviceability Analysis of Composite Concrete Slabs with Steel Decking

A Creep and Shrinkage Prediction Model for Serviceability Analysis of Composite Concrete Slabs with Steel Decking

Early shrinkage experiment of concrete and the development law of its temperature and humidity field in natural environment

Concrete shrinkage is one of the main causes of structural deterioration, and clear understanding of the development law of temperature and humidity field is a key to accurately predict concrete shrinkage. To study the effect of temperature and humidity (T&H) difference of concrete caused by curing conditions on its shrinkage, this study designs a concrete shrinkage experiment with three different maintenance conditions of moisture-thermal exchange, moisture retention, and moisture-thermal retention. Subsequently, a data correlation algorithm combining maximum information coefficient (MIC) and random walk (RW) is proposed. The correlations between the environment T&H and the concrete T&H, concrete temperature and its humidity, as well as the concrete T&H and its shrinkage are quantitatively investigated. The experimental results indicate that the curing conditions of moisture retention and moisture-heat retention respectively reduce the concrete shrinkage strain by 30.7% and 11.3% compared with the moisture-heat exchange condition at the age of 125 days. The coupling effect between the change rate of temperature and the change rate of humidity needs to be considered in the analysis of the moisture and heat field of concrete, while the influence of the second derivative of temperature with respect to space on the humidity change rate (that is, the Soret effect) can be ignored. The conclusions obtained in this study can provide a basis for the selection of coupling terms in the moisture and heat transfer analysis of concrete and the establishment of shrinkage prediction models.

Quality prediction and control of thin-walled shell injection molding based on GWO-PSO, ACO-BP, and NSGA-II

Abstract ECG recorders are precision medical devices, but their thin-walled shells are susceptible to warpage and shrinkage during injection molding production due to the injection molding process, which greatly shortens their service life. To address this problem, a multiobjective optimization method for injection molding process parameters based on a combination of a BP neural network model optimized by an ant colony algorithm (ACO-BP) and an improved non-dominated sorting genetic algorithm (NSGA-II) is proposed. The study takes the warpage deformation amount and volume shrinkage rate of the plastic part as the optimization objectives, and the melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time as the design variables. However, for BP neural networks, it is crucial to choose an appropriate number of hidden layer neurons, so the particle swarm algorithm combined with the grey wolf algorithm (GWO-PSO) is used to solve for the optimal number of hidden layer neurons. Firstly, the number of hidden layer neurons of the BP network model was solved based on the samples obtained from the Box–Behnken experimental design and the GWO-PSO algorithm, and the ACO-BP algorithm was used to build the prediction models for warpage and volume shrinkage, respectively, and then combined with NSGA-II for global optimisation. The pareto optimal solution set was subjected to CRITIC analysis and the optimal process parameters were finally obtained, with a minimum warpage of 0.3293 mm and minimum volume shrinkage of 4.993%, a reduction of 8.93 and 6.95% respectively compared to the pre-optimisation period. At the same time, injection molding tests were carried out on the optimum process parameters, and it was found that the molding quality of the plastic parts was better and met the actual production requirements through measurement. The research in this paper provides a theoretical basis for further improving the quality defects of the thin-walled injection molded parts.