Strength Prediction Model Research Articles

Investigation on compressive strength and splitting tensile strength of manufactured sand concrete: Machine learning prediction and experimental verification

Due to experimental constraints, traditional strength prediction models for manufactured sand concrete (MSC) exhibit significant limitations and have a narrow range of applicability. To overcome these limitations and enhance prediction accuracy, this paper, based on the widespread application of machine learning in concrete performance research, employed four algorithms: Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) to develop highly accurate and strongly generalizable models for predicting the compressive strength (CS) and splitting tensile strength (STS) ofMSC. The predictive performance of each model was evaluated, the influence laws of various factors were analyzed, and finally, macroscopic experiments were conducted to validate the models' prediction accuracy and generalization ability. The development of these models and the analysis of the influence laws of various factors on the strength of MSC provide a valuable reference for the mix design. The conclusions are as follows: (1) The XGB model demonstrates the highest prediction accuracy and strongest generalization ability for both the CS and STS of MSC, achieving R2 values of 0.98 and 0.94 on the testing set, respectively. (2) The CS and STS of MSC are primarily influenced by four factors: water-binder ratio (W/B), curing age (AGE), cement (C), and coarse aggregate (RI≥0.08869, RI: relative importance), showing a positive correlation with AGE and C, and a negative correlation with W/B. As the fly ash content, stone powder content, maximum particle size of coarse aggregate, and sand ratio increase, the CS and STS initially increase and then decrease. Appropriately increasing the fineness modulus of manufactured sand is beneficial to both CS and STS, while the addition of slag is beneficial to CS and harmful to STS. (3) The prediction accuracy and generalization ability of the XGB model are verified through three sets of macroscopic experiments: C30, C35, and C40. The relative errors of each set's predictions are less than 8 %. (4) A graphical user interface is constructed based on the XGB model, enhancing its practicality.

A systematic framework of constructing surrogate model for slider track peeling strength prediction

A systematic framework of constructing surrogate model for slider track peeling strength prediction

Response Surface Design Models to Predict the Strength of Iron Tailings Stabilized with an Alkali-Activated Cement

Tailing storage facilities are very complex structures whose failure generally leads to catastrophic consequences in terms of casualties, serious environmental impacts on local biodiversity, and disruptions in the mineral supply. For this reason, structures at risk must be reinforced or decommissioned. One possible option is its reinforcement with compacted filtered tailings stabilized with binders. Alkali-activated binders provide a more sustainable solution than ordinary Portland cement but require an optimization of the tailing–binder mixture, which, in some cases, can lead to a substantial experimental effort. Statistical models have been used to reduce the number of those experiments, but a rational design methodology is still lacking. This methodology to define the right mixture for a required strength should consider both the mixture components and in situ conditions. In this paper, response surface methods were used to plan and interpret unconfined compression strength test results on an iron tailing stabilized with alkali-activated binders. It was concluded that the fly ash content was the most important parameter, followed by the liquid content and sodium hydroxide concentration. From the obtained results, several statistical models were defined and compared according to the definition of a strength prediction model based on a mixture index parameter. It was interesting to observe that models with the porosity cement index still provide reasonable adjustment even when different tailings’ water contents are considered.

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.

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.

Machine learning assisted design of new ductile high-entropy alloys: Application to Al-Cr-Nb-Ti-V-Zr system

The search for new high-entropy alloys (HEAs) with desired properties is an urgent problem that is hardly solvable experimentally due to the extremely large number of possible alloy compositions. Thus, methods for theoretical prediction of HEA's properties play a key role. Currently, effective predictive models are based on machine learning methods and modern data analysis algorithms. Here we address developing data-driven machine learning models (DDML) to predict the ductility of HEAs. We have built several DDMLs and found that the best approach is based on the Support Vector Classifier, which significantly outperforms phenomenological models (balanced accuracy of 0.784 and F-score of 0.824). By combining this model with a previously developed yield strength prediction model, we have predicted and fabricated novel HEAs of the Al-Cr-Nb-Ti-V-Zr system with good mechanical properties. An obtained Al1Cr9Nb35Ti5V40Zr10 alloy demonstrates a combination of high strength at room and elevated temperature, combined with good ductility at room temperature.

Regression prediction model for shear strength of cold joint in concrete

The shear resistance of cold joint in concrete is influenced by various design parameters. Traditional mechanical models typically consider only a limited number of parameters to predict the shear strength of cold joints. This research aims to explore the complex nonlinear relationship between design parameters and cold joint shear strength using statistical method and machine learning technology. The goal is to develop a more accurate, reliable, and engineering applicable regression model for predicting shear strength. A dataset of 546 Z-shaped shear specimens characterizing cold joint in concrete was constructed, involving a total of 16 variables that may affect shear performance. Correlation analysis and recursive elimination were adopted to eliminate correlated and insignificant variables based on their importance. Multiple linear regression (MLR), random forest regression (RFR), and support vector machine regression (SVR) prediction models for cold joint shear strength were established based on rigorously screened variables and comprehensively evaluated using multiple methods. It was found that the most significant factors influencing the shear strength of cold joints are concrete strength, interface shear key, product of interface reinforcement strength and its reinforcement ratio, normal stress, fiber length of new concrete, casting method of the new concrete, and product of the fiber length and its tensile strength of old concrete. The MLR, SVR, and RFR models all exhibited superior performance relative to traditional mechanics-based models with regard to shear strength prediction of cold joints. The RFR model is recommended for predicting the shear strength of cold joints due to its superior evaluation indexes in comparison to the MLR and SVR models, and variable sensitivity analysis shows that it does not yield common-sense errors.

An energy-based framework for predicting vehicle noise source intensity: From energy consumption to noise

The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17–40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

Influence of surface roughness on the deformation of gold nanoparticles under compression

The influence of surface roughness on the mechanics of faceted gold nanoparticles under compression is investigated using molecular dynamics simulations. Results show an increasing impact of the surface roughness on the mechanical response while decreasing the roughness parameters with critical strength variations up to 90% of the one computed in case of flat-surface nanoparticles. Surface ledges act as stress concentrators and nucleation sites for the emergence of dislocations in regular 1/2〈110〉{111} slip systems and less common 1/2〈110〉{001}. Moreover, rough surfaces evolve continuously during deformation as influenced by a dislocation surface shearing process that impacts the dislocation nucleation process. Finally, the yield stress of nanoparticles shows a strong dependence to the true contact surface with two distinct regimes that depends on whether dislocations nucleate from an isolated surface atomic islet or from an edge-connected one. A model for strength prediction in nanoparticles relying on the surface topography is proposed. This study offers new perspectives on the interpretation of critical stress data scattering often measured in mechanical experiments performed at the nanoscale.

Prediction of Rock Unloading Strength Based on PSO-XGBoost Hybrid Models.

Rock excavation is essentially an unloading behavior, and its mechanical properties are significantly different from those under loading conditions. In response to the current deficiencies in the peak strength prediction of rocks under unloading conditions, this study proposes a hybrid learning model for the intelligent prediction of the unloading strength of rocks using simple parameters in rock unloading tests. The XGBoost technique was used to construct a model, and the PSO-XGBoost hybrid model was developed by employing particle swarm optimization (PSO) to refine the XGBoost parameters for better prediction. In order to verify the validity and accuracy of the proposed hybrid model, 134 rock sample sets containing various common rock types in rock excavation were collected from international and Chinese publications for the purpose of modeling, and the rock unloading strength prediction results were compared with those obtained by the Random Forest (RF) model, the Support Vector Machine (SVM) model, the XGBoost (XGBoost) model, and the Grid Search Method-based XGBoost (GS-XGBoost) model. Meanwhile, five statistical indicators, including the coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), were calculated to check the acceptability of these models from a quantitative perspective. A review of the comparison results revealed that the proposed PSO-XGBoost hybrid model provides a better performance than the others in predicting rock unloading strength. Finally, the importance of the effect of each input feature on the generalization performance of the hybrid model was assessed. The insights garnered from this research offer a substantial reference for tunnel excavation design and other representative projects.

Explainable machine-learning-based prediction of QCT/FEA-calculated femoral strength under stance loading configuration using radiomics features.

Finite element analysis can provide precise femoral strength assessment. However, its modeling procedures were complex and time-consuming. This study aimed to develop a model to evaluate femoral strength calculated by quantitative computed tomography-based finite element analysis (QCT/FEA) under stance loading configuration, offering an effective, simple, and explainable method. One hundred participants with hip QCT images were selected from the Hong Kong part of the Osteoporotic fractures in men cohort. Radiomics features were extracted from QCT images. Filter method, Pearson correlation analysis, and least absolute shrinkage and selection operatormethod were employed for feature selection and dimension reduction. The remainingfeatures were utilized as inputs, and femoral strengths were calculated as the ground truth through QCT/FEA. Support vector regressionwas applied to develop a femoral strength prediction model. The influence of various numbers of input features on prediction performance was compared, and the femoral strength prediction model was established. Finally, Shapley additive explanation, accumulated local effects, and partial dependency plotmethods were used to explain the model. The results indicated that the model performed best when six radiomics features were selected. The coefficient of determination (R2), the root mean square error, the normalized root mean square error, and the mean squared erroron the testing set were 0.820, 1016.299 N, 10.645%, and 750.827 N, respectively. Additionally, these features all positively contributed to femoral strength prediction. In conclusion, this study provided a noninvasive, effective, and explainable method of femoral strength assessment, and it may have clinical application potential.

Performance degradation evaluation of square steel tubes with local penetration damage

Steel tubular structures are susceptible to welding defects, cracking defects and environmental corrosion, which can negatively affect the stability and reliability of structure. This study tested the axial compression performance of twenty-six square steel tubular stubs with localized penetration damage simulated by artificial notch, considering the influence of notch length, orientation, and position. The test results showed that the failure mode of the specimens was dominated by the local buckling along the direction of the notch axis. The failure mode of the specimen with vertical notch showed notch opening, while those with horizontal notch showed notch closing, resulting in the secondary peak value in the load-displacement curve. The existence of local penetration damage reduced the ultimate strength and ductility of the tubular stubs. Compared to the ultimate strength of the specimens with middle notch, those with corner notch were weakened more, up to 27 %. Furthermore, the effects of notch dimension, position, orientation, and tube wall thickness on the axial bearing capacity of square steel tubular stubs are discussed by using a validated finite element model. Owing to the analysis, the ultimate strength of the stubs is reduced by increasing notch length, width and depth, whereas the reduction effect tends to be weakened as the notch orientation angle increases. The notch located in the corner is the most unfavorable for the axial compression performance of the square steel tubular stubs. A semi-empirical formula for evaluating the ultimate strength of square steel tubular stubs with local penetration damage is proposed by introducing the strength reduction factor. An artificial neural network-based ultimate strength prediction model is developed that can consider the effects of wide-range parameters, achieving high accuracy.

Machine-Learning-Assisted Composition Design for High-Yield-Strength TWIP Steel

Twinning-induced plasticity (TWIP) steel is an ideal material for impact-resistant structures and energy absorption because of its high product of strength and elongation. However, compared with other advanced high-strength steels, the relatively low yield strength of TWIP steel is one of the important shortfalls that significantly limits its engineering applications. To enhance the comprehensive properties of TWIP steel, a machine learning design strategy that integrated comparative modelling, SHAP analysis, and multi-objective optimization were adopted in this study. Initially, various machine learning algorithms were compared for their predictive accuracy based on normalized data (273 entries) regarding the microstructure and properties of TWIP steel. Then, performance prediction models for yield strength, tensile strength, and elongation were established. SHAP analysis was subsequently employed to assess the significance and explicit laws of composition and microstructures in these three target properties, identifying key elements that enhance the overall performance. Furthermore, two new TWIP steels with high yield strengths and high products of strength and elongation were developed via multi-objective optimization. Under conventional hot forging + hot rolling + cold rolling + annealing processes, the two designed TWIP steels had yield strengths of 585 MPa and 560 MPa, tensile strengths of 1055 MPa and 1101 MPa, elongations of 55% and 58.5%, and products of strength and elongation of 58.0 GPa% and 66.4 GPa%, respectively. The yield strengths of the designed TWIP steels significantly improved while maintaining a reasonable product of strength and elongation. This work provides important references for the rational development of new TWIP steels.

AI-based non-linear models for mechanical and toughness properties of sustainable fiber-reinforced geopolymer concrete (FRGPC)

The incorporation of fibers into geopolymer concrete (GPC) produces FRGPC which mitigates brittle failure and restrains macro crack propagation; however, research on predicting the mechanical and toughness properties of FRGPC remains limited. This study addresses this gap by developing prediction models for compressive strength (CS), flexural strength (FS), flexural toughness (FT), and fracture toughness (FR). A dataset of 600 data points was compiled from the published literature, encompassing various constituent proportions, fiber shapes, fiber dosages, aspect ratios, and curing conditions. Employing an artificial neural network (ANN) methodology, 10 independent variables (g1, g2, …., g10) were utilized to predict four dependent variables (CS, FS, FT, and FR), resulting in the development of eight non-linear ANN models for both straight fibers (SFs) and hooked fibers (HFs). Each model has shown a higher R 2 value and lower root mean square error (RMSE) for training (70%), testing (15%), and validation (15%) datasets. The prediction model for the CS of FRGPC with HFs (CS-HF) and SF (CS-SF) showed R 2 values of 0.983 and 0.973, and RMSE of 2.088 and 2.435 MPa highlighting the accuracy of the ANN models. Similarly, the comparative analysis for FR models exhibited R 2 values of 0.997 and 0.987, and RMSE values of 0.045 Mpa √m and 0.032 Mpa √m for FR-HF and FR-SF, highlighting that fiber addition strongly impacts improving toughness properties. To identify the most influential independent variable(s), sensitivity analysis revealed g10, g1, g8, and g9 as the most influential parameters for mechanical and toughness properties with SFs. For HFs, g1, g2, and g3 were most influential for mechanical properties, and g8, g9, and g10 for toughness. This study also presented the mathematical formulation of the developed models for better interpretability and to facilitate optimal and economical FRGPC mix design selection, highlighting the potential of AI-based models in advancing sustainable construction materials.

Analysis of damage evolution in single‐lap and double‐lap bolted joints of carbon fiber reinforced polymer plates based on load distribution

AbstractIt is important to study damage evolution as well as failure of carbon fiber reinforced polymer (CFRP) plates bolted joints. Therefore, the quasi‐static tensile test on single‐lap and double‐lap CFRP plates bolted joints was conducted. Meanwhile, the tensile strength prediction model for titanium alloy bolted joint of CFRP plates was established based on the improved 3D Hashin failure criterion, then a theoretical model was proposed to accurately predict the load distributions and tilt angle of bolted joints. Thus, the load–displacement curves were divided into four stages, and load distributions at different stage points as well as the relationship between tilt angle of bolt and load variation were obtained. The damage evolution of single‐lap and double‐lap at different stage points was analyzed respectively, and failure mechanisms were revealed based on load distribution. The results show that the ultimate failure of single‐lap is caused by the intrusion of bolt head into CFRP plates, while double‐lap is caused by the compression deformation of central plate. The numerical simulation works are in high agreement with the experimental results.Highlights Calculate the load distribution at single‐lap and double‐lap bolted joints. Obtain the relationship between bolts tilt angle and load variation. Analysis of damage evolution in bolt‐hole wall of carbon fiber reinforced polymer (CFRP) plates. Using loads to reveal the failure mechanisms of single‐lap and double‐lap bolted joints of CFRP plates.

Soft computing models for prediction of bentonite plastic concrete strength

Bentonite plastic concrete (BPC) is extensively used in the construction of water-tight structures like cut-off walls in dams, etc., because it offers high plasticity, improved workability, and homogeneity. Also, bentonite is added to concrete mixes for the adsorption of toxic metals. The modified design of BPC, as compared to normal concrete, requires a reliable tool to predict its strength. Thus, this study presents a novel attempt at the application of two innovative evolutionary techniques known as multi-expression programming (MEP) and gene expression programming (GEP) and a boosting-based algorithm known as AdaBoost to predict the 28-day compressive strength ( ) of BPC based on its mixture composition. The MEP and GEP algorithms expressed their outputs in the form of an empirical equation, while AdaBoost failed to do so. The algorithms were trained using a dataset of 246 points gathered from published literature having six important input factors for predicting. The developed models were subject to error evaluation, and the results revealed that all algorithms satisfied the suggested criteria and had a correlation coefficient (R) greater than 0.9 for both the training and testing phases. However, AdaBoost surpassed both MEP and GEP in terms of accuracy and demonstrated a lower testing RMSE of 1.66 compared to 2.02 for MEP and 2.38 for GEP. Similarly, the objective function value for AdaBoost was 0.10 compared to 0.176 for GEP and 0.16 for MEP, which indicated the overall good performance of AdaBoost compared to the two evolutionary techniques. Also, Shapley additive analysis was done on the AdaBoost model to gain further insights into the prediction process, which revealed that cement, coarse aggregate, and fine aggregate are the most important factors in predicting the strength of BPC. Moreover, an interactive graphical user interface (GUI) has been developed to be practically utilized in the civil engineering industry for prediction of BPC strength.

Hybrid soil strength prediction model for geotechnical ground investigation using convolutional neural network and ensemble learning

Abstract A hybrid model of soil strength prediction from the image data and feature data of soil was proposed, which is aimed at providing an efficient solution in geotechnical ground investigation by leveraging the power of AI. In this model, CNN purely with the encoder was used to establish the relationship between image data and strength parameters of soil, while ensemble learning stacked of three base learners, i.e., KNN, LASSO, and LSVM, was applied to the feature data. Then, the CNN part and the ensemble learning part were integrated into the loss function. Results show that the convergence of the hybrid model was slower than that of the ensemble learner. However, with the aid of soil image which has more features of soil in essence, the predicted soil strengths by the hybrid model better matched with the actual ones, compared with the ensemble learner and base learners. This hybrid model offers an effective framework of multi-modal data fusion for geotechnical engineering, by leveraging the synergy of the high-dimensional feature extraction capabilities of CNN and the generalization abilities of ensemble learning.

Data-driven shear strength predictions of prestressed concrete hollow-core slabs

Although providing sufficient shear strength is crucial in the design of hollow-core slabs (HCS) to prevent shear failure, conventional shear equations for HCS could not always represent the non-linear relationship between the shear strength and influencing parameters, especially for deep HCS. Back-propagation-neural-network- (BPNN)-based models could be a possible solution since they have been widely used in predicting structural behaviour of other concrete structures. However, the feasibility of such an application has not been examined previously. Therefore, a set of BPNN-based models with and without optimisation algorithms were proposed for predicting the shear strength of prestressed HCS. The proposed BPNN-based models were trained and verified against a database of 227 HCS tested in shear. Comparisons between BPNN-based and mechanics-based models were also performed. The corresponding performances of different models were evaluated by the mean Vut/Vpre, R2, RMSE, MAE and MAPE criteria. To the author's best knowledge, this is the first study to apply hybrid-BPNN-based models for shear strength prediction of HCS. The study found that all proposed BPNN models were effective in predicting the shear strength, while the ACO-BPNN model yielded the best predictions among all the BPNNs. It was also shown that the ACO-BPNN model provided better predictions than the ACI 318 code provisions with/without the size effect factor. Besides, the well-trained ACO-BPNN model could present the size effect of slab thickness on shear strength, which was not explicitly considered in ACI 318. In summary, the ACO-BPNN-based model could a rational tool for predicting shear strength for HCS.

Enhancing concrete strength prediction models with advanced machine-learning regressors

Enhancing concrete strength prediction models with advanced machine-learning regressors

Deformation field evolution characteristics of layered cemented paste backfill and strength prediction based on ultrasonic pulse velocity

ABSTRACT 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.