Model For Prediction Of Compressive Strength Research Articles

A Prediction Model for the Unconfined Compressive Strength of Pervious Concrete Based on Mix Design and Compaction Energy Variables Using the Response Surface Methodology

Pervious concrete is desirable for water drainage in building systems, but achieving both high strength and good permeability can be challenging. Also, the importance of compaction energy is significant in determining the efficiency of pervious concrete. However, research on the development of unconfined compressive strength (UCS) prediction models for pervious concrete materials that incorporate compaction energy parameters remains unexplored. Therefore, this study aimed to balance strength and permeability while optimizing the compaction energy required for concrete production. A Central Composite Design (CCD) was used to design experiments within the response surface methodology (RSM) and evaluate the UCS, the porosity and permeability of pervious concrete specimens produced with varying cement content (280.00–340.00 kg/m3), the water-to-cement ratio (0.27–0.33), the aggregate-to-cement ratio (4:1–4.5:1), and compaction energy (represented by VeBe compaction time, 13–82 s). A regression model with goodness of fit (R2adjusted > 0.87) was calibrated to estimate the UCS of pervious concrete as a function of mix design parameters and VeBe compaction time (Tvc). This model can potentially guide field practices by recommending compaction strategies and mix designs for pervious concrete, achieving a desirable balance between mechanical strength and hydraulic permeability for building construction applications.

Robust prediction models for flow and compressive strength of sustainable cement grouts for grouted macadam pavement using RSM

This study investigates the utilization of pumice stone ash in cementitious grouts for grouted macadam pavement, aiming to enhance flowability and compressive strength. Partial replacement of ordinary Portland cement (OPC) with varying percentages (5–20 %) of powdered pumice stone (PPS) ash was explored, alongside adjustments in water-to-cement (w/c) ratios. Experimental assessments, including flowability tests and compressive strength evaluations, were conducted on fresh and hardened grouts. Response surface methodology (RSM) and ANOVA analysis were employed to analyze the relationship between pumice stone ash content, w/c ratio, flow, and compressive strength. The results show that increasing the PPS content from 0 % to 20 % led to an increase in flow time from 23.6 to 34 seconds at a 0.3 w/c ratio, from 9 to 19 seconds at a 0.35 w/c ratio, and from 7 to 14 seconds at a 0.40 w/c ratio: indicating reduced flowability of the grouts. The 7-day compressive strength increased by approximately 2–17 % when 5–15 % of the cement was replaced with PPS, but a reduction occurred at 20 % PPS. Similarly, the 28-day compressive strength increased by about 4–23 % with 5–15 % PPS, while 20 % PPS led to a decrease in strength. The results highlight specific combinations of pumice stone ash content and w/c ratios, particularly 10 % PPS at 0.35 w/c and 15 % PPS at both 0.35 and 0.40 w/c, that meet grouted macadam standards, balancing flowability and strength. Fit statistics demonstrate the reliability of the predictive model. Optimization aims to maximize pumice stone ash content and w/c ratio, with graphical representation indicating an optimal composition at 17.25 % PPS and 0.35 w/c ratio for efficient grouted macadam construction. Additionally, Significant contribution towards sustainability can be achieved by replacing cement with pumice stone ash in pavement construction.

Experimental Study on Mechanical Properties and Compressive Constitutive Model of Recycled Concrete under Sulfate Attack Considering the Effects of Multiple Factors

To investigate the mechanical properties and a compressive constitutive model of recycled concrete under sulfate attack considering the effects of multiple factors, two waste concrete strengths (i.e., C30 and C40), four replacement ratios of recycled coarse aggregates (i.e., 0, 30%, 50% and 100%), and two water–cement ratios (i.e., 0.50 and 0.60) were considered in this study, and a total of 32 recycled concrete specimens were designed and tested. The results indicated that the failure processes and patterns of recycled concrete were not significantly influenced by the replacement ratio of recycled coarse aggregates, the waste concrete strength, the water–cement ratio, or sulfate attack. The higher the replacement ratio of recycled coarse aggregates and the water–cement ratio and the lower the waste concrete strength, the more obvious the reduction in cubic compressive strength, with a maximum reduction of 38.48%. A prediction model for the cubic compressive strength of recycled concrete under sulfate attack was proposed. The higher the replacement ratio of recycled coarse aggregates and the water–cement ratio and the lower the waste concrete strength, the more significant the reduction in axial compressive strength, with a maximum reduction of 37.82%. A prediction model for the axial compressive strength of recycled concrete under sulfate attack was established. A compressive constitutive model of recycled concrete under sulfate attack considering the effects of the replacement ratio of recycled coarse aggregates, the waste concrete strength, and the water–cement ratio was established. The pore structure of recycled concrete was significantly destroyed by the expansion stress generated by Na2SO4 crystals: a large number of Na2SO4 crystals were attached to the surface of concrete matrix, and the concrete matrix became loose. The research results can provide a theoretical basis and data support for engineering applications of recycled concrete.

Experiments and design of laminated bamboo and concrete-filled steel tube columns under axial compression

To reduce the large amount of carbon emissions generated during concrete production and increase the application of green and sustainable building materials in the engineering field, laminated bamboo is used to replace the core section of concrete-filled steel tubes. The effects of cross-sectional forms and core section types on the failure modes and axial compressive properties of column structures are evaluated. The axial compressive behaviors of 16 bamboo columns, bamboo-steel tube (BST) columns, and concrete-filled steel tube (CFST) columns were compared from the perspectives of strength-to-weight ratio, stiffness, and absorbed energy. The experimental results show that the failure modes of laminated bamboo and concrete-filled steel tube (BCFST) columns are end buckling failure and diagonal shear failure. The axial compressive strength of the BST column increased by 94.49 % due to the restraining effect of the steel tube on the bamboo. The key finding is that the cross-sectional area of the core bamboo is a critical factor affecting the axial compressive strength. The BCFST columns with circular cross-sections show excellent ductility, with higher ductility factors of 111.50 % and 30.24 % than BST and CFST columns, respectively. In addition, laminated bamboo is found to be an excellent energy-absorbing material compared to other materials. Based on the confinement effect of BST columns, a prediction model for the axial compressive strength of BCFST columns is proposed, by which the predicted value shows a good agreement (overall error is less than 10 %) with the experimental results.

Development of Prediction Models for Compressive Strength in Cement Mortar with Bentonite Using Machine Learning Techniques

In this study, the effects of bentonite-blended cement mortar, cement strength, cement amount, slump values, water absorption percentages by weight, and porosity values on the 28-day compressive strength were investigated using MR (Multiple Regression), ANFIS (Adaptive Neuro-Fuzzy Inference System), and the intuitive optimization method known as PSO (Particle Swarm Optimization). Based on the results obtained from 18 data points, with 4 of them used for testing and 14 for training, effective and ineffective input parameters were identified in comparison to MR. Subsequently, PSO and ANFIS main models were designed according to the obtained results. As a result of the study, it was determined that cement strength, cement amount, and water absorption parameters have a higher impact on compressive strength compared to other parameters. It was found that the best accuracy model was achieved with the PSO model, and the results of the MR model can also be used in predicting outcomes.

In-depth insight into the driving factors of the compressive strength development of MKPC based on interpretable machine learning methods

Magnesium potassium phosphate cement (MKPC) is a kind of Mg-chemically bonded phosphate ceramic commonly used for rapidly repairing dilapidated structures. In this study, a compressive strength dataset of MKPC was constructed, and four advanced machine learning (ML) algorithms (XGBoost, RF, GBDT and ANN) were selected to establish a high-precision compressive strength prediction model of MKPC. The SHAP and PDP methods are also used for interpretability analysis of ML-MKPC models. The XGBoost model has good generalizability and reliability while achieving high prediction accuracy. The RF and GBDT models performed similarly to the XGBoost model on the training set but performed poorly on the testing set. The ANN model is poorly trained on both the training and testing sets, with a risk of underfitting. The R2 of the XGBoost model at the different compressive strength stages still reaches above 0.80, indicating that it not only captures the complex relationships of the overall dataset well but also effectively predicts the staged strength dataset. Feature importance analysis revealed that the curing age (T), water-to-binder ratio (W/B), mineral admixtures-to-binder ratio (MA/B) and phosphate-to-magnesium ratio (P/M) are the principal variables affecting the compressive strength of MKPC. The partial interpretation shows that the optimum value range is determined when W/B is 0.10–0.18, MA/B is 0–0.20, P/M is 0.40–1.0, and R/M is 0–0.12. The composition of mineral admixtures with high-Ca, high-Si and low-Al systems seems to be more conducive to participating in the hydration reaction of MKPC. The ML-MKPC compressive strength prediction model developed in this study can provide theoretical support for the subsequent composition design and performance optimization of MKPC.

Cost Optimization of Graphene Oxide-Modified Ultra-High-Performance Concrete Based on Machine Learning Methods

The use of carbon nanomaterials in ultra-high-performance concrete (UHPC) to improve its mechanical properties and durability is growing. Graphene oxide (GO) has emerged as one of the most promising nanomaterials in recent years for enhancing the properties of UHPC. The majority of research so far has been on the properties of UHPC enhanced with GO, but its high cost has limited its application in engineering. This work suggests a machine learning (ML)-based approach to optimize the mix ratio in order to lower the cost of graphene oxide-modified UHPC. To do this, an artificial neural network (ANN) is used to create the prediction model for the 28-day compressive strength and slump flow of UHPC. The performance of this model is then compared using nine different ML techniques. Subsequently, considering the restrictions of the UHPC component content, component proportion, and absolute volume, a genetic algorithm (GA) is adopted to lower the UHPC cost. The sensitivity analysis is carried out in the end. This study’s findings indicate that there is a decent degree of prediction accuracy since the difference between the ANN model’s predictions and the experimental outcomes is just 10%. The cost of UHPC optimized by GA is reduced to 776 $/m3, significantly lower than the average cost of UHPC.

Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches

Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.

Concrete Compressive Strength Prediction by Ensemble Machine Learning Approach

The prediction of concrete compressive strength is a crucial aspect of ensuring the structural integrity and durability of construction projects. In recent years, machine learning approaches have improved upon the limitations of empirical formulas and laboratory testing methods for predicting concrete compressive strength. This study utilizes ensemble machine-learning techniques, such as Bagging, XGBoost, and Stacking models, to enhance the accuracy of concrete compressive strength prediction models. A five-fold cross-validation technique was applied to mitigate the problems of underfitting or overfitting in the regression model. Furthermore, various statistical indices were employed to compare the forecasting performance of these ensemble techniques. The prediction performance of this research revealed that XGBoost achieved the highest R-squared value of 93%, followed by Stacking and Bagging regression models at 92%. Consequently, this research underscores the potential of ensemble techniques as valuable tools in the domain of civil engineering, paving the way for more reliable and efficient construction practices.

Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models

Prediction model for compressive strength of rice husk ash blended sandcrete blocks using a machine learning models

Machine learning guided iterative mix design of geopolymer concrete

Current mix design methods for Geopolymer concrete (GPC) require substantial efforts of trial-and-error experiments and is applicable only to those formulated by specific precursor materials. In this work, a machine learning (ML) guided mix design method for GPC is proposed, which can considerably reduce the experimental workload and be versatile with a broad range of precursor materials. First, a database for the slump and compressive strength of GPC was established, and ML prediction models were developed with these as objectives. Subsequently, the mix design for GPC was conducted using the particle swarm optimization (PSO) algorithm. The designed mixture proportions underwent experimental validation, and if targets were not met, results were then used for model iteration until desired performance targets were achieved. The results showcase the excellent performance of the established slump and compressive strength prediction models, with an accuracy and coefficient of determination of 94 % and 0.95, respectively. The developed ML guided model effectively generates concrete mix achieving compressive strengths of 20 MPa, 40 MPa, and 60 MPa, while concurrently satisfying slump requirements. The experimentally validated results for the three designated target strengths after iterations were 25.2 MPa, 43.8 MPa, and 66.6 MPa, respectively. These positive results emphasize the ability of the proposed optimized mix design method to meet both the strength and workability targets in GPC via a minimum number of trail experiments.

Multi-objective optimization design of recycled aggregate concrete mixture proportions based on machine learning and NSGA-II algorithm

This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation.

Statistical Reliability Analysis of Ultrasonic Velocity Method for Predicting Residual Strength of High-Strength Concrete under High-Temperature Conditions.

Herein, we conducted a comprehensive statistical assessment of the ultrasonic pulse velocity (UPV) method's effectiveness in predicting concrete strength under diverse conditions, specifically early age, middle age, and high-temperature exposure. The concrete mixtures, with water-to-cement (W/C) ratios of 0.33 and 0.28, were classified as granite aggregate or coal-ash aggregate mixes. Compressive strength and UPV measurements were performed under these conditions, and subsequent statistical analyses treated the identified factors as distinct groups. The results revealed a substantial difference in compressive strength between specimens at early age (average of 13.01) and those at middle age (average of 41.96) and after high-temperature exposure (average of 48.08). Conversely, UPV analysis showed an insignificant difference between the early-age specimens and those after high-temperature exposure. The analysis of the W/C ratio and coarse aggregate demonstrated significant differences (p-value < 0.05) in compressive strength between specimens in middle age and those exposed to high temperatures, excluding the early-age specimens. However, UPV analysis revealed insignificant differences, with p-values of 0.67 and 0.38 between specimens at an early age and post-high-temperature exposure, respectively. Regression analysis identified suitable functions for each scenario, emphasizing the importance of a strength prediction model for concrete after high-temperature exposure, particularly considering the W/C ratio. Since concrete showed statistically different compressive strength, UPV, and strength prediction models in three conditions (early age, middle age, and high temperature), different strength prediction models must be used for the purpose of accurately predicting the strength of concrete.

Study on Coordinated Deformation Failure Mechanism and Strength Prediction Model of Rock-lining Concrete

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.

Compressive Strength Prediction Model of High Strength Concrete by Destructive and Nondestructive Technique

Concrete’s compressive strength can be tested in a laboratory before construction begins. Since concrete is a natural material and cannot be destroyed, it is not possible to determine its compressive strength through destructive testing. Rebound hammers are typically used in the field to evaluate the structural elements’ ability to withstand hardened concrete. As part of the current study, a comparison was made between concrete’s compressive strength measured by destructive testing and its surface hardness measured by rebound hammering. Tests were conducted on laboratory-made concrete cubes in this study to determine destructive and non-destructive behavior. Minitab software was used for regression analysis. Schmidt rebound hammer tests, a type of nondestructive testing (NDT), were shown to have very strong relationships with concrete destructive compression tests. Schmidt rebound hammers are commonly used to measure the surface hardness of concrete, since the hammer rebound number and concrete strength are theoretically correlated. Utilising a Schmidt hammer, it was applied. Standard concrete cubes with crushing strengths between 20 and 30 MPa were created using various mix proportions. Using regression analysis, destructive and non-destructive values are correlated. The linear regression equation is well suited for obtaining the compressive strength using rebound value by using linear regression equation.

Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning.

Portland cement concrete (PCC) is a major contributor to human-made CO2 emissions. To address this environmental impact, fly ash geopolymer concrete (FAGC) has emerged as a promising low-carbon alternative. This study establishes a robust compressive strength prediction model for FAGC and develops an optimal mixture design method to achieve target compressive strength with minimal CO2 emissions. To develop robust prediction models, comprehensive factors, including fly ash characteristics, mixture proportions, curing parameters, and specimen types, are considered, a large dataset comprising 1136 observations is created, and polynomial regression, genetic programming, and ensemble learning are employed. The ensemble learning model shows superior accuracy and generalization ability with an RMSE value of 1.81 MPa and an R2 value of 0.93 in the experimental validation set. Then, the study integrates the developed strength model with a life cycle assessment-based CO2 emissions model, formulating an optimal FAGC mixture design program. A case study validates the effectiveness of this program, demonstrating a 16.7% reduction in CO2 emissions for FAGC with a compressive strength of 50 MPa compared to traditional trial-and-error design. Moreover, compared to PCC, the developed FAGC achieves a substantial 60.3% reduction in CO2 emissions. This work provides engineers with tools for compressive strength prediction and low carbon optimization of FAGC, enabling rapid and highly accurate design of concrete with lower CO2 emissions and greater sustainability.

Strength Prediction of Non-Sintered Hwangto-Substituted Concrete Using the Ultrasonic Velocity Method.

This paper presents and investigates the properties of concrete in which a portion of the cement is substituted with non-sintered Hwangto (NSH), a readily available building material in Asia. Given the inactive nature of NSH, this study aimed to determine the optimal cement replacement ratio and quantitative strength of the material. The unit weight, compressive strength, ultrasonic pulse velocity (UPV), and stress-strain of the NSH concrete (NSHC) were evaluated. Additionally, we developed a predictive model for determining compressive strength based on the regression analysis of compressive strength and UPV. The water-to-binder ratio was set to 0.41, 0.33, and 0.28, and the NSH replacement rates in the cement were set to 0%, 15%, 30%, and 45% for evaluating various strength ranges. The mechanical property measurements indicated reductions of 5.35% in unit weight, 35.62% in compressive strength, and 6.34% in UPV as the NSH was replaced. Notably, the smallest deviation from plain concrete was observed at a replacement rate of 15%. The scanning electron microscopy analysis results showed that the plain concrete exhibited a crystalloid structure; however, as the NSH replacement rate increased, the amorphous structure and pores increased while unreacted NSH particles were also observed. The X-ray diffraction analysis results demonstrate that the peak intensities for kaolinite and mullite increased as the NSH replacement rate increased, while those of C-S-H gel and CaO showed low peak intensities. Furthermore, the regression analysis concluded that an exponential function was suitable. Consequently, a compressive strength prediction model was developed, and in the error test, the NSHC model demonstrated an average error of <10%, with fewer errors at the lower compressive strength boundaries.

Machine Learning Based Compressive Strength Prediction Model for CFRP-confined Columns

The study described in the paper focuses on the compressive strength of concrete columns that are reinforced with carbon fiber reinforced polymer (CFRP). The researchers aimed to identify the main parameters that affect the compressive strength of these confined columns. They used models to analyze and study the relationship between these parameters and the compressive strength. The study found that certain parameters had an inverse relationship with compressive strength. These parameters include the column diameter, CFRP fracture strain, and modulus of elasticity. On the other hand, the CFRP thickness and concrete strength exhibited a positive relationship with the compressive strength. The study also determined that the influence of column diameter and CFRP thickness was greater compared to the influence of CFRP fracture strain and elastic modulus on the compressive strength. To predict the compressive strength, the researchers developed a machine learning algorithm-based compressive strength prediction model. They found that a backpropagation (BP) neural network model showed high prediction accuracy and robustness in predicting the strength. Additionally, the researchers' model was analyzed, and it was found that while the calculated values from their model aligned well with the experimental results, there were some issues with overestimating or conservatively estimating the compressive strength in certain cases.

Prediction model of compressive strength of foamed concrete considering pore size distribution

To more accurately evaluate the relationship between pores and compressive strength of foamed concrete, the effects of density, water-cement ratio, and foaming agent type on porosity, pore size distribution, and compressive strength were studied. It was found that as the density decreased and the water-cement ratio increased, the number of larger-sized pores increased and the pore size distribution became wider and more non-uniform. Further, the pore structure of lower-density foamed concrete was more susceptible to the influence of density and water-cement ratio more likely to produce larger size pores. Particularly, foaming agent type had a significant effect on the pore size distribution. The pore size variation of foamed concrete was primarily attributed to the initial size of fresh foam and the complex merging and escape phenomena occurring in the foam in the slurry, the process of preparation and forming. It was also found that smaller porosity and more concentrated and uniform pore size distribution were more beneficial for strength. Additionally, to quantify the combined effect of porosity and pore size distribution on compressive strength, this paper innovatively proposes the parameters of the “pore synthesis impact factor”. Finally, a new empirical model relating the porosity, size distribution, and compressive strength is presented.

Evaluation on Early Strength Development of Concrete Mixed with Non-Sintered Hwangto Using Ultrasonic Pulse Velocity.

Currently, in order to reduce the greenhouse gases of global warming, research on alternative cement materials is being actively conducted in the construction industry to reduce cement use, and it is judged to be important to evaluate the timing of form removal for the initial age. Therefore, in this study, we evaluated the initial mechanical properties of concrete in which cement was partially replaced with non-sintered hwangto (NHT). Specimens without NHT (namely, normal mortar (NM) and normal concrete (NC)) and specimens with NHT (namely, non-sintered hwangto mortar (HTM) and non-sintered hwangto concrete (HTC)) were prepared. NHT was substituted for 15% and 30% of cement. Two water-to-binder (W/B) ratios, 41% and 33%, were used to analyze the variation in the mechanical properties according to the cement and NHT content per unit volume of concrete. The compressive strength and ultrasonic pulse velocity (UPV) were measured. Experimental results indicated that compressive strength decreased with an increase in NHT content. The mortar with NHT substitution rates of 15% and 30% exhibited higher UPV than NM at a W/B ratio of 41%, in contrast to the behavior observed for concrete. The UPVs of most specimens were similar regardless of the NHT substitution rate. The correlation between the compressive strength and UPV of HTC was analyzed, and therefrom, exponential equations with a high correlation coefficient (R2) were proposed for strength prediction; the resulting predictions were compared with the results of previous compressive strength prediction models.