Method for optimizing proportion of cement stabilized macadam base of recycled aggregate

Abstract

The invention relates to a proportion optimization method for a cement stabilized macadam base of recycled aggregate. Taking an interface failure sensitivity coefficient IFSI as a quantitative index; the method comprises the following steps: performing microstructure reconstruction on recycled aggregate particles through SEM scanning, resistivity testing and CT scanning, extracting two indexes of defect density and surface adsorption strength, and constructing a degradation prediction model ITZ; the ITZ result is used as a basic parameter for adjusting the water cement ratio and the fine aggregate content, so that a mechanism from aggregate defects to interface failure is connected; establishing unit volume interface energy consumption minimization as an optimization target based on an IFSI value output by the interface failure model; the influence of the water cement ratio, the grading difference and the fine particle wrapping degree on the interface bonding energy consumption is analyzed, and a matching point under the specific recycled aggregate condition is found out through a multivariable function coupling model.

Description

Technical Field

[0001] The invention relates to a method for optimizing the proportion of cement-stabilized crushed stone base layers of recycled aggregate. Background Art

[0002] The current mix proportioning method for cement-stabilized crushed stone base has been initially applied in engineering practice, but there are still many significant deficiencies and systematic drawbacks, which restrict the stability of the long-term service performance of recycled materials in road base and the scientific nature of mix proportioning design, which is mainly reflected in the following aspects: First, the current mix proportioning design method is mostly based on the extension of natural aggregates, and a special optimization system for the differences in the characteristics of recycled aggregates has not yet been established. Recycled aggregates have complex sources and large performance fluctuations, especially in terms of crushing rate, porosity, surface residual glue layer, mud content and other aspects. They are significantly different from natural aggregates. If traditional mix proportioning theory (such as water-cement ratio empirical method, graded intercalation theory, etc.) is directly applied, it is very easy to cause problems such as insufficient hydration in the interface area, uneven porosity, and cementation failure, thereby forming an "innate structural weak zone", which seriously affects the uniformity of material strength and early stability;

[0003] Secondly, traditional methods lack the ability to model and quantify the microscopic behavior of the interface transition zone (ITZ). The interface layer is the location where recycled aggregate is most likely to fail during use, but most optimization designs only focus on the overall unconfined strength or the final value of the 28-day compressive strength, and fail to consider the microscopic interaction processes such as the bonding properties, chemical reaction rate, and hydration product type between the recycled aggregate and the cement paste, resulting in an imbalance in the structural performance of the interface area, causing cracks to appear and spread in the early stages of service, affecting life prediction and the formulation of crack control strategies; thirdly, current proportioning designs are mostly based on static indicators, such as a single particle size distribution curve, a fixed cement content or a mineral powder ratio, etc., and lack a dynamic performance evaluation mechanism during service life. The structural integrity and water absorption rate of the recycled aggregate itself determine its performance in dry-wet cycles, high-temperature exposure or repeated loads. Under the action of the load, faster physical disintegration and interface peeling will occur, and the traditional method fails to establish the correlation path between material response and time-environment-load, resulting in premature structural failure in actual projects, increased maintenance costs, and difficulty in ensuring the design life; Fourthly, the existing methods often ignore the coupling relationship between construction process parameters and material properties, especially when key parameters such as compaction energy, initial moisture content, and curing humidity fluctuate greatly, which has a significant impact on the density and early hydration rate of the recycled aggregate water-stabilized base structure. The traditional mix design only measures the strength value under standard indoor conditions, and does not conduct a systematic linkage analysis of the on-site rolling energy, cement reaction kinetics and aggregate interface state, which can easily lead to a serious disconnect between the laboratory design value and the on-site measured value, further reducing the practicality and reliability of the design scheme;

[0004] Fifth, data application is still lagging behind. Current optimization methods mainly rely on manual experience, decentralized experiments, and single-variable adjustment strategies, lacking a unified data platform and multivariate collaborative analysis tools. There is a high degree of nonlinear coupling between the variables involved in the proportion of recycled aggregates (such as particle size, water absorption, coating thickness, pH value, initial setting temperature rise, cement mineral composition, etc.). Single-variable regulation cannot effectively capture the essential path of material performance changes, and thus cannot establish a proportioning model with strong predictiveness and high versatility. It also limits the promotion and use of intelligent material design methods such as machine learning in recycled material systems. Finally, the use of interface enhancement methods is disconnected. Although some projects have introduced additional components such as silica fume, mineral powder, and expansion agents, they lack a targeted blending mechanism that matches the microstructure of recycled aggregates. The addition method is often conventional mixing or dry blending, which cannot achieve precise activation of the interface area and spatial focusing of material functions, resulting in low admixture efficiency and delayed reaction. It cannot effectively solve the problems of slow interface reaction and asynchronous hydration, and ultimately leads to a decrease in the overall homogeneity of the structure. Summary of the Invention

[0005] The object of the present invention is to provide a method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate, thereby solving some of the drawbacks and deficiencies pointed out in the background art.

[0006] The present invention solves the above-mentioned technical problems by adopting the following technical solution, which includes the following steps:

[0007] Using the interfacial failure sensitivity coefficient (IFSI) as a quantitative indicator, the microstructure of recycled aggregate particles was reconstructed through SEM scanning, resistivity testing, and CT scanning. Two indicators, defect density and surface adsorption strength, were extracted to construct a degradation prediction model, ITZ. The ITZ results served as a basic parameter for adjusting the water-cement ratio and fine aggregate content, connecting the mechanisms from aggregate defects to interfacial failure.

[0008] Based on the IFSI value output by the interface failure model, minimizing the interfacial energy dissipation per unit volume is established as the optimization goal; the effects of water-cement ratio, gradation difference, and fine particle encapsulation on the interfacial bonding energy consumption are analyzed, and the mixing point under specific recycled aggregate conditions is found through a multivariable function coupling model.

[0009] Furthermore, the ITZ degradation prediction model is further developed including:

[0010] Control the local hydration rate imbalance caused by residual glue and rough structure on the surface of recycled aggregate, and establish a hydration activity mismatch index HAI to evaluate the synchronization of the hydration process; design an auxiliary admixture system containing active regulating components including Ca(OH)2 slow-release particles + mineral powder to adjust the local pH and C3S rate in the initial stage of cement hydration; control the synchronization of the hydration reaction rate, product distribution, and pore structure formation between the interface area of ​​the recycled aggregate and the interior of the matrix, and optimize the early strength formation path.

[0011] Furthermore, the construction of the hydration activity mismatch index HAI includes:

[0012] The concept of construction density index (CDI) is adopted to couple the actual compaction energy, moisture content and predicted strength growth curve. A dual-channel detection system for on-site compaction and hydration is adopted: the density is read in real time through the rebound modulus during the compaction stage, and the temperature rise rate and hydration heat release data are used to predict the strength growth potential during the initial setting stage. The key construction parameters such as on-site moisture content and number of rolling passes are adjusted according to feedback, so that the construction process and the laboratory proportioning model are dynamically closed.

[0013] Furthermore, the construction of the hydration activity mismatch index HAI includes:

[0014] The recycled aggregate properties, interface energy consumption, strength growth curve, and compaction parameters are input into a multi-field coupled damage evolution model to predict crack development and strength degradation during the material's service life. The potential failure threshold output by the damage evolution model is mapped to the initial mix ratio model, and the original mix ratio is corrected based on feedback from risk points.

[0015] In the above scheme, the microphysical properties of recycled aggregate, interfacial energy consumption behavior, cement hydration strength growth curve and on-site construction compaction parameters are used as system variables and input into the newly constructed damage evolution response function model to predict the crack development path and strength decline trend of the base layer during its service life; according to the potential failure threshold output by the model, the initial design ratio parameters are adjusted in reverse, thereby constructing a closed-loop adjustment system between material design, service performance and structural response.

[0016] The material damage evolution prediction function is used to express the performance evolution of the internal structure of the recycled aggregate cement-stabilized crushed stone base during its service life in a multivariable integral coupling manner. The material performance response function Φ(t) is defined to represent the effective structural retention rate (residual strength ratio) at time t, and its form is as follows:

[0017]

[0018] The meaning of each parameter in the formula is:

[0019] Φ(t): structural retention rate, which represents the performance ratio of the material at time t relative to the initial strength; ρb : crushing degree of recycled aggregate, characterizing the stability of aggregate particle structure; γ e : interfacial energy consumption density, representing the energy loss capacity of the aggregate-paste interface; κ c : the early slope of the strength growth curve, reflecting the strength growth efficiency in the early stage of hydration reaction; ω: construction compaction density factor, indicating the influence of compaction degree on the distribution of cracks in the later stage; ψ R (ρ b ): Aggregate crushing damage function, which is customized as the fatigue degradation sensitivity weight of recycled aggregate; ψ I (γ e ): interface energy dissipation function, simulating the effect of microcrack accumulation on structural stability; ψ C (κ c ,ω): coupled compaction-hydration function, describing the interactive effect between construction and hydration strength; α, β, θ: weight coefficients, adjusted according to material grade and engineering environment; λ(τ): time decay function, representing the cumulative effect of various stress cycles under service environment; τ: integral time variable, internal processing dimension of service time; dτ: integral variable, time infinitesimal.

[0020] This function model Φ(t) decreases with time and is used to determine the durability evolution characteristics of the material. When Φ(t) drops to the set failure threshold Φ crit When Φ(t) is between 0.6 and 0.7, the model automatically determines that the structure has entered the stage of crack expansion or critical strength degradation. By extracting the sub-variable in the function that is most sensitive to Φ(t), the main controlling factor of the material's potential failure can be determined. The system returns this information to the proportion model database and executes the following correction logic:

[0021] If ψ R (ρ b ) control item is dominant, it is suggested to increase the proportion of fine aggregate or use recycled materials with low crushing rate;

[0022] If ψ I (γ e ) control item dominates, then introduce interface strengthening admixtures (such as silica fume, polymer);

[0023] If ψ C (κ c If the fluctuation of the control item is large, it is suggested to strengthen the compaction quality control or improve the initial cement reactivity;

[0024] Finally, a closed-loop path from damage evolution prediction → failure threshold identification → ratio optimization and adjustment is completed, realizing an intelligent material ratio optimization method for service performance.

[0025] Furthermore, the construction of the active regulating component auxiliary admixture system includes:

[0026] Ca(OH)2 slow-release particles are added to the mixture, and alkaline ions are gradually released within the initial 2 to 4 hours to increase the local pH value of the interface area of ​​the recycled aggregate; the mineral powder and hydration products are used to react synergistically to enhance the interfacial activity and promote the formation of gel-like structures; the Ca(OH)2 slow-release particles are coated with organic polymers or inorganic coating materials to control the release rate and duration of alkaline ions.

[0027] Furthermore, the mineral powder is added in an amount of 10% to 25% of the total mass of the cementitious material, and is used to participate in the formation reaction of the interface cementitious structure in the early stage of hydration; the auxiliary admixture system is based on recycled aggregate particles, and is distributed in a direction in the interface area through surface pre-spraying, pre-mixed coating or dry mixing.

[0028] Furthermore, the damage evolution model is used including the following steps:

[0029] S1: Collection of recycled aggregate characteristics:

[0030] The key physical and mechanical properties of recycled aggregates are collected, including but not limited to:

[0031] Crushing rate (indicating the structural integrity of aggregate particles);

[0032] Porosity (representing the total amount of internal voids);

[0033] Residual glue content (reflects the residual original cement binder on the surface).

[0034] S2: Obtaining interface energy consumption and strength growth parameters:

[0035] The following performance parameters are obtained as model input:

[0036] Interface energy consumption parameter (representing the damage energy consumption capacity of the interface micro-region);

[0037] Material early strength growth curve (reflecting hydration rate and reaction degree);

[0038] Construction compaction parameters (such as compaction degree, rebound modulus, etc., indicating construction density).

[0039] S3: Multi-source parameter coupling modeling:

[0040] The multi-source variables obtained from S1 and S2 are input into the multi-field coupled damage evolution function to establish a response prediction model for damage accumulation over time to reflect the structural evolution behavior of materials under the influence of multiple field factors such as traffic loads, climate change, and dry-wet cycles.

[0041] S4: Failure threshold output and ratio feedback mechanism:

[0042] The model outputs a potential failure threshold, including the interface microcrack accumulation value, strain concentration area, and strength drop rate; a mapping relationship is established between this threshold and the original mix parameters (such as gradation, water-cement ratio, and mineral admixture ratio), so as to provide feedback corrections to high-risk component parameters and form a mix optimization mechanism based on service prediction.

[0043] To express the damage evolution process described above, the present invention constructs the following original integral function to quantify the performance retention rate of recycled aggregate cement stabilized base during its service life:

[0044]

[0045] The characters and their meanings in the formula are:

[0046] D(t): Damage accumulation function value of the material at time t, used to indicate the degree of degradation of service performance (the larger D(t), the lower the residual performance); where t is the current service time under consideration and also the upper limit of the integral;

[0047] τ: integral variable, representing the length of the service cycle from the initial time to the current time; η1, η2, η3: coupling weight coefficients, used to control the influence intensity of different damage mechanisms, which can be calibrated by experiments; μ(χ r ):Recycled aggregate damage potential energy function, its input variable χ r The comprehensive index of aggregate crushing rate, porosity and residual glue content; φ(∈ i ,ΔE): interface energy consumption function, where∈ i is the microscopic interface strain concentration factor, ΔE is the unit energy consumption of the interface; ξ(ν c ,ρ d ): compaction-strength coupled response function, where ν c is the early strength growth rate of the material, ρ d is the degree of compaction (density); σ(τ): environmental response function, used to simulate the time-varying service environment excitation intensity, including comprehensive effects such as temperature and humidity fluctuations and load cycles; t: the current service time under investigation (in months or years), which is also the upper limit of the integral.

[0048] Application of damage function in the model and feedback path:

[0049] When D(t) exceeds the set critical value D crit When , it is judged that the material structure has reached the potential failure risk;

[0050] The model will analyze the function term (such as μ(χ r ) represents aggregate problems, φ represents interface problems, etc.);

[0051] Map the initial matching parameters corresponding to the dominant risk item and feed them back into the matching design model;

[0052] Implement the following remediation policy example:

[0053] If μ is too high: use recycled aggregate with more uniform particle size and more complete structure;

[0054] If φ is too high: introduce an interfacial modifier or a micro-expansion component;

[0055] If ξ shows stress concentration: mineral admixtures that increase compaction or increase early reaction rate; further, the damage evolution model is a multi-field joint numerical model of coupled mechanics, moisture migration and temperature effects, which is used to dynamically evaluate the material performance degradation path; the recycled aggregate characteristic parameters include aggregate particle size distribution, mud content, water absorption rate and residual rubber coating thickness.

[0056] Furthermore, the interface energy dissipation parameters are obtained through micro shear tests or interface adhesion tests and are used to evaluate the energy dissipation characteristics between aggregate and paste.

[0057] Furthermore, the damage evolution model identifies potential failure risks by dynamically comparing the design strength with the simulated strength trend curve, and marking the crack leader area and the interface weak zone; the proportion correction includes optimizing the fine aggregate ratio, cement content or interface reinforcement components.

[0058] The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate proposed in the present invention has the following significant beneficial effects:

[0059] By constructing a multi-field damage evolution model that couples mechanics, hydration, temperature, and moisture migration, we dynamically simulate the crack development and strength degradation paths of materials under real service environments, enabling scientific predictions from the microscopic properties to the macroscopic performance of materials. This allows the proportion design to no longer rely on experience, but instead have a quantifiable basis for performance control.

[0060] Emphasizing the regulation of physical and chemical activity in the interface zone, the interfacial energy consumption parameter (ΔE) and hydration activity mismatch index (HAI) are innovatively introduced as evaluation indicators, which significantly improves the structural stability of the interface between recycled aggregate and cement paste, and improves the "weak bonding zone" problem that is prone to occur in traditional recycled base layers.

[0061] Multiple parameters such as the crushing rate, porosity, residual glue content, particle size distribution, and mud content of the recycled aggregate are input into the damage evolution function. At the same time, data such as micro-shear tests and strength growth curves are introduced to construct a feedback mechanism to achieve a dynamic closed loop of material design, experimental evaluation, and ratio adjustment, thereby improving the long-term service reliability of the base layer.

[0062] By directional distribution control of auxiliary admixture systems (such as Ca(OH)2 slow-release particles + mineral powder) in the interface area, the early hydration reaction of the material is made more uniform and the structural bonding is denser, which not only enhances the strength and crack resistance, but also takes into account factors such as on-site mixing technology, construction compaction and moisture content adjustment, ensuring that the plan is feasible and replicable. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of the method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to the present invention.

[0064] Figure 2 The present invention is a flowchart of the method for constructing the active regulating component auxiliary admixture system.

[0065] Figure 3 This is a flow chart of the damage evolution model construction method of the present invention. DETAILED DESCRIPTION

[0066] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0067] Combine Figure 1 The process and optimization method for cement-stabilized crushed stone base mixes using recycled aggregates is based on a performance prediction model based on the microscopic characteristics of the interface, fundamentally achieving scientific control of mix parameters (such as water-cement ratio and fine aggregate content). The core of this method is the development and application of the Interfacial Failure Sensitivity Index (IFSI) as a key indicator to quantify the degradation potential of aggregate interfaces.

[0068] The specific operation begins with the acquisition of multi-source microscopic data. Scanning electron microscopy (SEM) is used to perform high-resolution imaging of the surface micromorphology of the recycled aggregate, identifying the morphological characteristics of cracks, micropores, and residual adhesive films on the particle surface. Resistivity testing is then used to assess the electrical conductivity of the particle interior and interface regions, indirectly reflecting their pore structure and water retention capacity. Combined with industrial-grade CT scanning technology, the three-dimensional structure of the aggregate particles is reconstructed to accurately quantify microscopic damage characteristics, including crack connectivity, porosity, and coating thickness. The above experimental process requires sampling recycled aggregate of different particle sizes and different sources (such as demolished concrete and old bricks) to ensure representative data.

[0069] The acquired images and raw measurements undergo data preprocessing, including image binarization, boundary enhancement, and segmentation using a defect recognition algorithm. Ultimately, two key metrics are extracted: defect density, defined as the ratio of the number of cracks / voids per unit volume to the total aggregate volume, characterizes the internal structural integrity of the recycled aggregate; and surface adsorption strength, calculated by coupling image grayscale changes with resistivity, reflects the adsorption strength of the interfacial water film onto the cement paste. Based on these two key metrics, a predictive model for interfacial transition zone (ITZ) degradation is constructed. This model employs support vector regression (SVR) as its basic structure, with defect density and surface adsorption strength as input variables and an ITZ degradation grade (categorized as high, medium, and low) as output. The model is trained using over 50 sets of recycled aggregate microstructure data and their corresponding interfacial shear strength test results. All samples are normalized before training, with 80% of the samples used for training and 20% for validation. During training, kernel function parameters and penalty coefficients are optimized using 50-fold cross-validation, resulting in a stable and predictable ITZ degradation grade. By inputting this ITZ result into the mix optimization logic, a fixed water-cement ratio or empirical grading setting is no longer used. Instead, a targeted adjustment is made based on the recommended target water-cement ratio range and fine aggregate dosage interval corresponding to the ITZ grade. For example, when the ITZ degradation grade is high, a lower water-cement ratio and a higher proportion of fine aggregate are recommended to improve interface density and bonding performance, thereby achieving a closed chain of mechanisms from recycled aggregate micro-defects → interface transition zone performance → macro-mixing parameters, significantly improving the overall homogeneity and service reliability of the material.

[0070] Using the IFSI (Interface Failure Sensitivity Index) value output by the interface failure model as the basic parameter, the optimization goal is to minimize the interface energy dissipation per unit volume, thereby achieving structural tuning of the mix ratio scheme. In terms of technical approach, the IFSI value is first obtained using the aforementioned microstructure analysis and degradation prediction model. It represents the sensitivity of the recycled aggregate interface to failure forms such as microcracks and shear slip under stress.

[0071] In order to further transform the interface performance risk index into engineering parameter control logic, the present invention defines the unit volume interface energy dissipation function W IFZIt is expressed as the energy density consumed by the bond failure in the interface area. In order to quantify this energy consumption function, it is necessary to systematically collect data on a series of influencing factors, including cement paste fluidity and encapsulation force measurement data under different water-cement ratios, particle distribution density data under different grading and difference combinations, and fine particle encapsulation data obtained through microscopic image analysis. This data quantifies the area coverage and thickness distribution of fine aggregate at the interface through grayscale analysis and boundary segmentation algorithm. All experimental data need to be standardized and preprocessed, including outlier removal, range normalization, dimensional uniform conversion and other operations to ensure the stability and accuracy of subsequent multivariate modeling. After the data preparation is completed, a three-variable coupling function model W is established. IFZ =f(x1,x2,x3), where x1 represents the water-cement ratio, x2 represents the gradation difference (described by the ratio of the maximum particle size to the minimum particle size), and x3 is the fine particle packing degree. The goal is to make the function value W IFZ Minimum, that is, the energy consumption per unit volume of the interface is minimized, corresponding to the state with optimal bonding performance and lowest damage sensitivity.

[0072] The model structure is implemented by combining support vector regression (SVR) and response surface methodology (RSM). The SVR part is used to process nonlinear coupling relationships, and the RSM is used to construct the optimal solution domain under the multivariate response space. The SVR sub-model is trained through a large amount of experimental data (no less than 100 groups of different ratio combinations). At the same time, contour response maps and gradient directions are constructed to determine the relative influence weights of different variable combinations on energy consumption. During the model training process, k-fold cross-validation is used to prevent overfitting, and the model structure with the best generalization performance is found by adjusting the kernel function type (Gaussian kernel or radial basis kernel) and the penalty coefficient. After the training is completed, the IFSI value of the recycled aggregate is input into the model to locate its optimal ratio parameter area, which is specifically manifested as a certain optimal water-cement ratio range, gradation continuity design recommendations, and the minimum encapsulation threshold of fine particles.

[0073] To address the issues of hysteresis and reaction rate imbalance in the interfacial region caused by the residual adhesive layer, rough pore structure, and strong adsorption of recycled aggregate, an evaluation mechanism and admixture control system based on the "Hydration Activity Incompatibility Index (HAI)" was proposed to achieve synchronous regulation of the local hydration reaction environment, thereby optimizing the early strength development path. Specifically, the Hydration Activity Incompatibility Index (HAI) is a quantitative parameter used to reflect the differences in hydration rate, hydration degree, and product evolution between the interface region of recycled aggregate and the cement matrix. The construction of this index relies on the acquisition of microscopic hydration process data of the recycled aggregate mixture system. X-ray diffraction (XRD) is used to monitor the changes in the types and content of hydration products at different hydration ages. Differential scanning calorimetry (DSC) and simultaneous thermogravimetry (TG) are used to obtain the hydration heat release patterns in different regions. Micro-scanning electron microscopy (SEM) images and energy dispersive spectroscopy (EDS) analysis are also combined to capture the formation rate and structural density of CSH gel at the interface region.

[0074] Through the processing and comparison of the above multidimensional data, the HAI index model is constructed using differential integral analysis and dynamic time window sliding standard deviation method. The larger the value, the more significant the asynchrony between the interface area and the matrix area in hydration activity, reflecting the higher the potential risk of structural degradation. In order to reduce the HAI value and achieve synchronous hydration of the interface and matrix, the present invention designs an active regulating component auxiliary admixture system, the core of which is composed of Ca(OH)2 slow-release particles and mineral powder. The Ca(OH)2 slow-release particles use organic or inorganic coating materials to achieve phased alkali release within 2 to 4 hours, so as to quickly increase the local pH value of the recycled aggregate interface area and stimulate the hydration reaction of the C2S component; the mineral powder participates in filling and secondary reactions with a medium particle size distribution, releases active SiO2 in the early stage of hydration and forms a stable CSH skeleton structure, strengthening the bonding ability and density of the interface area.

[0075] This auxiliary admixture system preferentially acts on the surface of the recycled aggregate through a localized release mechanism, inducing hydration reactions at the interface to develop synchronously with the matrix, thereby promoting the formation of a continuous and stable network of hydration products throughout the entire material system at an early stage, significantly improving structural consistency and the rate of early strength development. This approach transcends the limitations of traditional methods of controlling hydration reactions by overall dosage, resolving interfacial activity imbalances through a targeted activation mechanism. This enables a synchronized control path from interfacial reaction rate to hydration product morphology to pore structure evolution, providing a precise, mechanism-level control solution for improving the performance of recycled material substrates.

[0076] Based on the concept of integrated construction-material response control, the Construction Densification Index (CDI) was constructed as a bridge to couple construction technology and material strength development, achieving a closed-loop mix control system from dynamic adjustment of construction parameters to real-time prediction of structural performance. The CDI (Compaction-Densification Index) is a composite indicator used to quantify the contribution of construction densification to hydration strength. Essentially, it couples actual on-site compaction energy (including number of compaction passes and roller excitation energy), initial mixture moisture content, and laboratory hydration strength growth curves to establish a response path from construction behavior to structural performance. During data collection, a proprietary, on-site dual-channel detection system was employed to capture key parameters. During the compaction phase, the roller's built-in vibration response sensor and a subgrade rebound modulus tester installed beneath the compaction zone captured real-time equivalent modulus data for each compaction area, reflecting compaction uniformity and density. During the initial setting and early curing phases, a network of embedded thermocouples, coupled with a wireless data acquisition module, recorded the temperature rise rate and heat release during the hydration exothermic process at high frequency, serving as a basis for assessing the potential for early cement hydration reaction strength growth. The acquired data was instantly uploaded to edge processing equipment and preprocessed, including outlier removal, time series smoothing, noise filtering, and synchronous calibration, before being input into a multivariate-integrated construction response function model.

[0077] The model uses support vector regression (SVR) as the core framework, and the input variables are compaction energy (J / m 2 ), moisture content (%), and initial temperature rise rate (°C / h). The output is the predicted strength development rate (MPa). The training data comes from a combined dataset of large-scale indoor tests and field measurements, consisting of approximately 120 samples. Training is performed in an 80% training, 20% validation manner. A grid search algorithm is used to optimize the model's penalty coefficient and kernel function parameters to ensure stable prediction capabilities across a variety of aggregate grades and weather conditions. The trained model can read compaction and temperature rise data in real time during actual construction, output a predicted structural strength development value under the current working conditions, and compare this value with the target strength growth path set in the laboratory mix model. If the deviation exceeds the set tolerance (e.g., ±10%), the system automatically sends a control signal back, prompting on-site technicians to adjust key construction parameters, such as increasing the number of compaction passes, adjusting the moisture content of the next load of material, or replacing slightly overaged material. Ultimately, through dynamic adjustments, the on-site construction status is coupled to the optimal laboratory mix, establishing a closed-loop regulation mechanism from "material design-field testing-feedback control."

[0078] Example 1:

[0079] Combine Figure 2In a road reconstruction project in an old urban area of ​​a city in Shandong, the construction company planned to use recycled concrete aggregate instead of natural aggregate in the cement-stabilized gravel base construction of the city's main arterial roads. The project route is 3.5 kilometers long, with a design life of 15 years and an expected moderate traffic load. Due to the high risk of interface performance degradation and crack development in recycled aggregates over long-term service, the project team decided to apply the principles of this invention for the first time to achieve a closed-loop optimization of material design, service performance, and construction control.

[0080] At the beginning of the project, technicians first collected raw material data according to the S1 stage of this method, and obtained the average crushing rate of recycled aggregate as ρ through image recognition and aggregate crushing value experiments. b = 24%, porosity is about 17%, residual adhesive rate is 9%; interface energy density γ e The micro shear test determined it to be 3.1 kJ / m 2 The early cement hydration reaction was fitted with the data of the test age of 3h, 6h, 12h and 24h to obtain the strength growth slope The compaction density factor ω = 0.92 is calculated from the ratio of the compaction data of the construction test section to the maximum dry density in the room. The project area is a typical service environment with large temperature difference between winter and summer, concentrated annual precipitation, and heavy vehicle load. The time decay function is set to λ(τ) = e -0.025τ , which is consistent with the attenuation rate of this type of climate stress. The technical team then established the material performance response function:

[0081]

[0082] The weight coefficients are: α = 0.5, β = 0.3, θ = 0.2, which represent the control strength of aggregate structure, interface energy dissipation and hydration-compaction matching respectively. Each damage sub-function is defined as follows: ψ R (ρ b )=0.02·ρ b =0.48,ψ I (γ e )=0.015·γ e =0.0465, Substituting the calculated damage function into the equation, we get:

[0083] D(t)=[0.5·0.48+0.3·0.0465+0.2·0.0899]·e -0.025t ≈0.271·e -0.025t

[0084] The total amount of accumulated damage (15 years, or 180 months) is calculated as follows:

[0085]

[0086] Φ(180)=exp(-10.84)≈1.95×10 -5

[0087] The results show that the structure has seriously degraded after 15 years and has almost lost its bearing capacity. Through variable sensitivity analysis, it was found that ψ R The dominant factor indicates that high crushing rate aggregates have the greatest impact on the long-term stability of the structure. The model feedback suggested reducing the crushing rate, increasing compaction, and improving early hydration efficiency. Therefore, the site decided to control the crushing rate below 18%, adjust the mineral powder content from 8% to 12%, and add 3% silica fume to improve interface density. The new parameters after adjustment are: b =18%, ω=0.9. Update the damage function parameters as follows: ψ R =0.36,ψ I =0.0345, The damage function is: D(t) = [0.5·0.36+0.3·0.0345+0.2·0.051]·e -0.025t =0.202·e -0.025t

[0088] Points earned:

[0089]

[0090] Φ(180)=exp(-8.08)≈0.00031

[0091] Although the performance is still declining, it has improved by an order of magnitude compared to before optimization. To further reduce the risk, the system recommends shortening the service design life to 12 years (t = 144). At this time:

[0092]

[0093] Combining traffic forecasts, cost assessments, and maintenance plans, the project team ultimately determined new construction mix parameters and designed a six-year thin-layer repair plan to achieve the optimal balance between performance, lifespan, and cost. During the model construction and verification process, the project team collected 160 sets of sample data with different recycled aggregates, water-cement ratios, admixtures, and construction densities. Using the Python platform, the SVR model and BP neural network were constructed to fit the function parameters. 10-fold cross-validation was used to evaluate the model accuracy. The final damage function fitting accuracy was R 2 The project achieved a value of 0.91, with an average error of less than ±5%. This project is the first in China to apply a micro-mechanism-driven intelligent damage evolution control model to a municipal road project. It verifies the feasibility of the invention in actual engineering scenarios and significantly improves the service safety and design foresight of recycled material base layers.

[0094] After improving the proportioning model, not only the crushing rate is reduced and the grading is adjusted from the physical level of the aggregate, but also the hydration process and the distribution of the gel product are further synchronized by precisely controlling the interface chemical reaction environment to solve the problem of insufficient structural consistency. To this end, the laboratory cooperated with the construction team to carry out a systematic verification experiment: first, three types of Ca(OH)2 slow-release particles were prepared under controlled conditions, which were coated with polyacrylate (PAA-Ca type), magnesium silicate layered material (MS-Ca type) and starch ether (ST-Ca type). The release rates reached 80%, 65% and 50% of the total content within 2 to 4 hours, respectively. The release process was measured by a pH online monitor and a conductivity analyzer. Among them, the particle size of the PAA-Ca slow-release particles is 250μm, the coating thickness is 12μm, and the control time window has the highest accuracy. Therefore, PAA-Ca type particles were selected as the formal engineering application component.

[0095] In terms of dosage, it was determined that PAA-Ca slow-release particles would be introduced at 2.5% of the total mass of the cementitious material, and mineral powder (S95 grade blast furnace mineral powder) would be added as an interface synergistic active source, with a dosage of 15% of the total amount of the cementitious material. In order to verify the interface activation ability of this combination, the technical team conducted micro-area pH tests (through fluorescent dye spectral analysis) in samples containing slow-release particles and those without particles. Within the hydration period of 2 hours, the interface pH of ordinary samples was about 10.3, and the interface pH of PAA-Ca-doped samples steadily increased to 11.6, effectively activating the early hydration reaction of C3S. In addition, through scanning electron microscopy (SEM) observation combined with energy spectrum analysis (EDS) detection, the continuity and density of the CSH gel in the interface area of ​​the sample doped with slow-release particles + mineral powder were significantly enhanced, which was about 28% higher than that of the undoped group, indicating that the spatial connectivity of the hydration products was significantly improved, effectively suppressing the "hydration blind spot" phenomenon.

[0096] Furthermore, in order to quantitatively evaluate the effect of this local pH control mechanism on the overall strength growth and structure retention function, the technical team re-determined the interface energy consumption density γe under this condition to be 2.1kJ / m 2 , early hydration slope κ c Increased to 0.106MPa / h 0. 5. The compaction density factor ω is maintained at 0.95, and the crushing rate is maintained at 18% after further gradation optimization. Substituting into the damage evolution function:

[0097]

[0098] The sub-functions are calculated as: ψ R =0.36,ψ I =0.0315,ψ C ≈0.0472, overall damage rate D(t)≈0.222×e(-0.025t) , the integral damage in 15 years (t=180) is about 6.58, then:

[0099] Φ(180)=exp(-6.58)≈0.00138

[0100] Compared to the previous optimization phase, which only adjusted aggregate grading without treating the interface (Φ≈0.00031), the structural retention rate increased by nearly 345% and showed a stable plateau trend. The model again predicted that the retention rate would be Φ(144)≈0.0033 after 12 years of service (t=144), meeting the structural safety threshold.

[0101] In order to verify the actual performance on site, 5 groups of mix ratios were set up in the project construction sample section, namely:

[0102] ① Control group (without slow-release particles), ② with 2% mineral powder, ③ with 2.5% PAA-Ca, ④ with 2.5% PAA-Ca + 10% mineral powder, and ⑤ with 2.5% PAA-Ca + 15% mineral powder. The same on-site rolling method and moisture content control conditions were used, and samples were taken at the ages of 7 days, 28 days and 90 days to test the unconfined compressive strength and splitting tensile strength. The results showed that the 28-day unconfined strength of group ⑤ reached 6.3 MPa, an increase of 18% compared with the control group. The 90-day structural strength retention rate was the highest, the splitting strength increased by 22%, and the extension length of the interface microcracks was reduced by more than 35%. Combined with acoustic emission testing and strain gauge array detection, it can be seen that this blending system has a stronger micro-damage delay ability under long-term service.

[0103] In terms of model training, a joint team of construction units and universities established a data set containing 210 sets of composite ratio-performance data. The variables included Ca(OH)2 particle coating method, release rate, pH dynamic value, mineral powder activity level, interface strength, etc. Random forest regression (RFR) was used to sort the importance of variables, SVR was selected to build a regression model, and GridSearchCV was used for kernel function tuning and cross-validation. The final model had a mean square error of 0.026 on the test set and R 2 The value is 0.93, which can accurately predict the changing trend of interface energy consumption under different blending ratios, and be connected as the input to the main function of damage evolution, providing data-driven support for the rapid screening of ratio parameters.

[0104] Subsequent optimization tests were carried out on the "directional distribution strategy of the auxiliary admixture system" proposed in this invention, with particular emphasis on the reaction efficiency, spatial distribution effect and interface performance improvement potential of mineral powder in the range of 10% to 25% in cementitious materials, and combined with three different methods to achieve controlled delivery in the interface area of ​​recycled aggregate particles.

[0105] The experiment was conducted in three phases: the first phase was an indoor evaluation of the cementitious reaction performance, the second phase was a verification of the actual interface distribution morphology, and the third phase was an integrated damage evolution model retraining and feedback correction. In the first phase, the technical team designed five dosage combinations, namely, mineral powder accounting for 10%, 15%, 18%, 20%, and 25% of the total mass of the cementitious material, all with a fixed proportion of Ca(OH)2 slow-release particles (2.5%). Samples were prepared under the same water-cement ratio (0.28) and the same compaction conditions. XRD analysis and SEM scanning at equal ages were used to identify the morphology of their interfacial hydration products. The results showed that the groups with dosages of 18% and 20% had the fastest CSH gel formation rate and the highest enrichment in the interface area in the early hydration period (6h to 24h). Among them, the 18% group formed a dense reaction shell layer with a thickness of 19μm, which was about 45% higher than the 10% group. In addition, the Si release amount also reached a peak as verified by plasma optical emission spectroscopy (ICP-OES). This data is incorporated into the model update input by the system and serves as a priority recommendation value.

[0106] In the second phase, to ensure the most effective application of the auxiliary admixture system at the recycled aggregate interface, the project team conducted comparative experiments using three different admixture methods: 1) surface pre-spraying: a 10% solids mineral powder slurry was sprayed onto the recycled aggregate surface and allowed to air dry for 12 hours; 2) pre-mix coating: a 3:1 ratio of dry mineral powder to pellets was mixed and tumbled in a rotary mixer for 6 minutes; and 3) conventional dry mixing: the admixture was added directly to the mixing system along with the other aggregates. All three groups maintained an 18% mineral powder content. SEM and image recognition technology were used to analyze the distribution of reaction products at the aggregate interface, preserving consistent early interfacial hydration characteristics. Statistically, effective interface coverage was 82% for the pre-spraying method, 71% for the pre-mix coating method, and only 48% for the dry mixing method, demonstrating significant differences. Furthermore, the shear bond strength at 7 days of age was compared. The sprayed group achieved 1.26 MPa, a 29% increase compared to the dry mix group, demonstrating that the targeted distribution significantly enhances the spatial efficiency of the gelling reaction.

[0107] In order to incorporate the above-mentioned interface behavior improvement effect into the overall damage evolution model and integrate it with the aforementioned structure retention function Φ(t), the team updated the database and refitted the mineral powder dosage and interface energy consumption function ψ in the model. I (γ e ) coupling expression, redefine γ e =f(%MP,D method ), where %MP is the amount of mineral powder added, D method is the distribution factor, and the setting values ​​are: spraying = 1.0, coating = 0.85, dry mixing = 0.6. The new interface energy consumption density obtained in the final model is: spraying group γ e =1.98kJ / m 2 , coating group γe =2.25, dry mix group γ e =2.61, substituted into the previous structure retention rate model:

[0108]

[0109] Keep the optimized aggregate parameter ρ b =18%,κ c =0.106, ω=0.95, and other weight coefficients are set as α=0.5, β=0.3, θ=0.2. The structural retention rate of the spraying method group samples at t=180 (months), that is, at the end of 15 years of service, is calculated respectively: ψ R =0.36,ψ I =0.015×1.98≈0.0297, ψ C ≈0.0472, total damage rate D(t)≈0.217×e (-0.025t) ,but:

[0110]

[0111] Based on actual conditions, while the structural retention rate is still relatively low, it is significantly better than that of an uncontrolled dry mix (Φ≈0.00006). Within 12 years, a Φ≈0.0021 could be achieved, meeting the service life expectations for this type of secondary road. The final project recommendation is to control the mineral powder content to 18%, using a surface pre-spraying method in combination with 2.5% slow-release alkali particles. This process is integrated during construction using a rotary spray device and forced mixing equipment to achieve standardized interface structure distribution and ensure repeatable material performance.

[0112] So far, the project has completed the verification of the entire path from micro-parameter acquisition, functional material design, reaction environment control to macro-ratio optimization and model closure, and established a demonstrative engineering case for the three-in-one base material design system of "recycled aggregate + intelligent model + interface orientation". It also verified the high engineering adaptability and service guarantee capability of the ratio optimization and interface activity regulation technology described in this invention.

[0113] Example 2:

[0114] Combine Figure 3 In a road upgrade and renovation project for a section of an urban arterial road in Shandong Province, in line with the principle of low-carbon recycling of building materials, the local government specified the use of recycled concrete aggregate instead of natural crushed stone for the cement-stabilized crushed stone base. Because recycled aggregates pose risks of strength degradation, microcrack propagation, and interface aging over long-term service, the project team fully incorporated the technical solutions of this invention, conducting systematic testing and modeling analysis to ensure structural stability over a design life of 15 years.

[0115] The project application process is strictly implemented in accordance with steps S1-S4 described in the present invention. Step S1 first completes the collection of the core physical properties of the recycled aggregate through on-site sampling and laboratory characterization. Using digital image analysis, crushing value experiments and CT reconstruction methods, the crushing rate of the recycled aggregate is obtained to be 26.3%, the average porosity is 16.7%, and the residual glue coverage is 12.5%. The three are comprehensively converted into the damage structure index χ r =0.263+0.167+0.125=0.555.

[0116] In step S2, the team obtained the interface unit energy dissipation ΔE=3.05kJ / m through the interface shear-tensile test. 2 , microscopic strain concentration factor ∈ i =0.82 (calculated by DIC image strain tracking method); and the slope of the early strength growth curve ν c (In the 3h–24h age Fitting) is 0.094MPa / h Compaction degree ρ d The construction compaction was determined by the ratio of the modulus of resilience to the maximum dry density. All data were normalized to the interval [0, 1] to meet the model input requirements.

[0117] In step S3, the team constructed the damage evolution function model as follows:

[0118]

[0119] The function terms are defined as follows: μ(χ r )=1.2·χ φ (∈ i ,ΔE)=0.8·∈ i +0.05·Δ, The environmental response function is set to σ(τ) = e -0.025τ , the coupling weight coefficients are η1=, η2=0., η3=0.2. Substituting into the calculation, we get: μ=1.2·0.555=0.666, φ=0.8·0.82+0.05·3.05=0.656+0.1525=0.8085, but:

[0120]

[0121] The target design life is 15 years (i.e. t = 180 months), and after integration we get:

[0122]

[0123] The critical failure threshold of the model is set as D crit=40, which means that the current structure is at risk of premature aging in service. Entering the S4 stage, the team conducted a sensitivity analysis of the contribution of the three sub-functions in D(t) and determined that the damage-dominant term is ξ(ν c ,ρ d ), that is, the compaction-strength synergistic effect is insufficient, followed by μ(χ r ), indicating that aggregate quality has a significant impact on damage. Therefore, based on the model feedback, the team implemented the following ratio optimization strategy: First, by improving the crushing and screening process and secondary particle shaping, the crushing rate was reduced from 26.3% to 20.1%, the porosity was reduced to 15.0%, and the residual glue was reduced to 10%. r =0.201+0.15+0.10=0.451, corresponding to μ=0.541; the second is to use early active admixtures with faster reaction (such as ultrafine mineral powder) to reduce ν c Increased to 0.115, ρ after on-site compaction control upgrade d Increased to 97.3%, new Then calculate the damage function:

[0124]

[0125] Points earned:

[0126]

[0127] Although still close to the critical value, the structural performance has been significantly improved. The model predicts that the structural retention rate will increase to approximately 76.5%, meeting the service performance requirements of municipal-grade roads. The model was ultimately trained on 50 sets of field working condition samples, and a predictor was constructed using GradientBoostingRegressor. The average error of the 5-fold cross-validation was controlled at ±3.6%. At the same time, the SHAP analysis method was used to further identify the explanatory power of the variables for D(t), ensuring that the model decision path is interpretable. This example verifies the high adaptability and predictive guidance capabilities of the ratio optimization mechanism of the present invention in actual engineering, truly realizing a closed-loop process from multi-parameter material input → damage evolution modeling → service life prediction → intelligent ratio correction.

[0128] With the challenges of large daily changes in the on-site environment, the arrival of the rainy season, and the actual construction of differences in recycled aggregate batches, the project team decided to further deepen the practicality and environmental adaptability of the model. Therefore, based on the aforementioned single-variable damage evolution function, it was expanded into a multi-field joint numerical model of coupled mechanics-moisture migration-temperature effects to more realistically and dynamically simulate the performance degradation path of the base material under complex service environments, especially under conditions of fluctuating recycled aggregate characteristics to improve the reliability of predictions and on-site guidance capabilities. In this phase, the recycled aggregate parameter collection dimensions were further enriched in the S1 data expansion. In addition to the already used crushing rate, porosity, and residual glue content, four new key control parameters were added: particle size distribution range D60 / D 10 =2.8, mud content 5.2%, water absorption 7.6%, and surface residual adhesive coating thickness (average thickness measured by an image analysis system) 92 μm. All parameters were obtained through batch sampling using particle image recognition, true density / apparent density difference method, standard blue adsorption test, and thin-layer microscopy. The data were merged and standardized (Z-score normalization) for input into the joint model.

[0129] Then, based on the S2 stage, the initial moisture migration parameters (moisture content distribution) after compaction and the temperature change curve within 7 days after construction (by burying thermistor arrays) were further introduced into the model. The team constructed a three-dimensional space-time distribution finite element mesh model and solved it based on the COMSOL Multiphysics platform using the "solid mechanics + unsaturated seepage + heat conduction" coupling module. The mechanics part used the nonlinear viscoelastic damage constitutive equation to model the expansion of interface microcracks, the moisture field simulated the pore water migration-adsorption-evaporation process, and the temperature field considered the synergistic effect of daily average temperature fluctuations (±13°C) and hydration heat release. The model boundary conditions were set as exposed road surface, full constraint on the bottom, weak lateral permeability, a time step of 12 hours, and a simulation period of 720 days (i.e. 2 years). The focus was on monitoring the crack strain concentration area, the matrix moisture content decrease rate, and the interface shear stress evolution.

[0130] In order to convert the simulation output results into structural performance prediction, the damage function is defined as:

[0131]

[0132] where σ m (τ, w, T) is a multi-field response function, which represents the comprehensive function of the moisture change gradient w, temperature fluctuation amplitude T and micro-area stress fluctuation accumulated over time τ, and is set as:

[0133]

[0134] Among them, a1=0.8 and a2=0.05, which are optimized by actual measurement and parameter sensitivity. The numerical simulation results show that under the conditions of recycled aggregate water absorption rate>7% and mud content>5%, the moisture gradient of the interface area near the area with the smallest particle size and the thickest residual glue layer is the largest within 60 days. As high as 0.46, the temperature difference between day and night causes ΔT to be around 13°C, which causes a sudden change in shear stress in the early condensation zone and triggers crack initiation.

[0135] During the model result feedback phase, the technical team selected the top 10% of high-damage units with cumulative D(t) values ​​in the simulation, reverse-mapped their input parameters, and identified the high-risk sources as residual adhesive coating thickness (dominant 28.4%), mud content (24.7%), water absorption (20.9%), and particle size discontinuity (16.3%). Four correction strategies were proposed: ① Control the residual adhesive layer thickness within 60 μm through pre-washing and ultrasonic-assisted cleaning; ② Control the mud content below 3.5%; ③ Improve aggregate drying and surface moisture control to suppress water absorption to 6.0%; ④ Optimize grading to reduce D 60 / D 10 Control within 2.2.

[0136] To further verify the effectiveness of the model correction, 6 stress-temperature and humidity monitoring points were set up for the optimized batch and the initial batch respectively after on-site construction. After 360 days of continuous monitoring, the comparison showed that the identifiability rate of interface cracks in the correction group decreased by 53%, the shear stress fluctuation amplitude decreased by 21%, and the interface moisture gradient decreased by 34%; the core sample drilling of the structural layer showed that the average residual strength of the base layer of the uncorrected group was 3.92MPa, and that of the corrected group was 4.67MPa, an increase of about 19%.

[0137] To establish a prediction and decision-making linkage mechanism, the project team built a complete multi-field coupling data-driven framework on the MATLAB platform and used the random forest (RF) regression model to predict the mapping relationship between the maximum growth rate of D(t) and the initial parameters. The training data consisted of 11,520 finite element units output by the numerical model. Each sample included 12 input features and D(t) values. Using 10-fold cross-validation, the final model achieved an RMSE of 0.143 and an R 2 =0.94, which can stably output high-risk allocation recommendations.

[0138] This embodiment then introduces the high-precision experimental acquisition of the interface energy consumption parameter ΔE and the linkage modeling of the crack development warning mechanism. In the laboratory, the team used a customized micro-shear loading device to perform uniaxial shear failure loading on the recycled aggregate-paste interface core sample cut from the site. The loading rate was controlled at 0.01mm / s, and the energy release was recorded in real time with the acoustic emission (AE) signal. Finally, the interface energy consumption per unit damage area was obtained by integration. Among the 45 groups of test samples, the average ΔE was 2.74kJ / m 2 , standard deviation 0.31, the numerical fluctuation is highly correlated with the unevenness of the aggregate residual glue layer. In addition, compared with another batch of samples using silica fume as the interface reinforcement component, the ΔE value is generally improved to 3.48kJ / m 2 The above shows that this parameter can be used as an important criterion for judging the quality of interface structure.

[0139] At the model level, the team compared the obtained ΔE value with the φ(∈i ,ΔE) terms, and combined with the structural strength trend curve evolving over time (with Φ(t) as the main indicator, derived from the early damage integral model), a comparison mechanism is introduced in the actual simulation: by dynamically monitoring the deviation between the design target strength curve and the model predicted strength curve When δ(t) exceeds the set threshold (such as 10%) for more than 12 hours, the finite element area corresponding to the time interval is marked as the "crack leader area", and the interface shear strain γ s The “interface weak zone” is identified in the clustering area, and the final output is in the form of a heat map superimposed on the model grid.

[0140] In the specific implementation, in the 5th unit grid of construction section 3, the model predicts 7-day age Φ sim =0.78, and the design target value Φ design =0.87, the difference is 10.3%, and the AE signal shows that the interface damage in this area is mainly concentrated in the cement-recycled aggregate wrapping zone, and the shear test ΔE=2.36kJ / m 2 , below the lower limit of the batch mean standard, the model also marked this area as a high-risk zone for crack initiation. Inverse analysis revealed that the fine aggregate ratio at this location was low (actually 18% compared to the designed 22%), the cement content was controlled at 3.5% (the lower limit), and there was no interface reinforcement component. Therefore, the model feedback optimization recommendations were as follows: increase the fine aggregate ratio to 24%, adjust the cement content to 4.5%, and introduce 3% silica fume as an interface-active reinforcement component.

[0141] To verify the optimization suggestion, the team set up a control sample section and an optimized sample section in the 6th construction unit, and compared the unconfined compressive strength at 28 days of age. The average of the control group was 5.62MPa, and the optimized group was 6.41MPa, an increase of 14%; the interface shear strength increased from 0.97MPa to 1.25MPa, an increase of 29%; the total energy decay time of the AE signal was extended from 47 seconds to 108 seconds, indicating that the development of microcracks was significantly slowed down; at the same time, the cumulative value of D(t) decreased from 8.82 of the original unit to 6.17 of the optimized unit, and the structural retention rate Φ(180) increased from 0.00018 to 0.0012. Although the value is still in the low range, the improvement trend is obvious.

[0142] In terms of data-driven modeling, the team used the ΔE, ∈ i , γ s After feature engineering processing of 45 sets of shear failure samples with the same parameters, Lasso regression was used to eliminate redundant features. Finally, XGBoost regressor was used to predict the Φ(t) decrease rate. The training set and validation set were split at 8:2. The final model was tested on the validation set R 2=0.91, MAE=0.052, demonstrating strong predictive performance and parameter interpretation capabilities. The model then conducted high-throughput simulation screening of different mix ratio combinations. Combined with construction process constraints (maximum moisture content not exceeding 8%, maximum binder content not exceeding 7%), it generated eight recommended mix ratios that achieved both structural and economic advantages. This guidance guided the construction of the subsequent three sections, forming a mix ratio feedback loop driven by shear energy consumption.

[0143] In summary, the damage evolution-interface energy consumption-strength trend linkage model described in the present invention forms a complete combination of theory and engineering path in this road project, and for the first time realizes a dynamic prediction and feedback system "from microscopic energy dissipation to macroscopic ratio optimization", significantly improving the safety and controllability of the performance of the recycled aggregate cement-stabilized gravel base structure, and providing a scientific allocation basis and decision-making model for the future large-scale application of recycled materials.

Claims

1. A method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate, characterized by: The following steps are included: Using the interfacial failure sensitivity coefficient (IFSI) as a quantitative indicator, the microstructure of recycled aggregate particles was reconstructed through SEM scanning, resistivity testing, and CT scanning. Two indicators, defect density and surface adsorption strength, were extracted to construct a degradation prediction model, ITZ. The ITZ results served as a basic parameter for adjusting the water-cement ratio and fine aggregate content, connecting the mechanisms from aggregate defects to interfacial failure. Based on the IFSI value output by the interface failure model, minimizing the interfacial energy dissipation per unit volume is established as the optimization goal; the effects of water-cement ratio, gradation difference, and fine particle encapsulation on the interfacial bonding energy consumption are analyzed, and the mixing point under specific recycled aggregate conditions is found through a multivariable function coupling model.

2. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 1, characterized in that: The ITZ degradation prediction model construction and deepening include: Control the local hydration rate imbalance caused by residual glue and rough structure on the surface of recycled aggregate, and establish a hydration activity mismatch index HAI to evaluate the synchronization of the hydration process; design an auxiliary admixture system containing active regulating components including Ca(OH)2 slow-release particles + mineral powder to adjust the local pH and C3S rate in the initial stage of cement hydration; control the synchronization of the hydration reaction rate, product distribution, and pore structure formation between the interface area of ​​the recycled aggregate and the interior of the matrix, and optimize the early strength formation path.

3. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 2, characterized in that: The hydration activity mismatch index HAI construction is further developed including: The concept of construction density index (CDI) is adopted to couple the actual compaction energy, moisture content and predicted strength growth curve. A dual-channel detection system for on-site compaction and hydration is adopted: the density is read in real time through the rebound modulus during the compaction stage, and the temperature rise rate and hydration heat release data are used to predict the strength growth potential during the initial setting stage. The key construction parameters such as on-site moisture content and number of rolling passes are adjusted according to feedback, so that the construction process and the laboratory proportioning model are dynamically closed.

4. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 3, characterized in that: The hydration activity mismatch index HAI construction is further developed including: The recycled aggregate properties, interface energy consumption, strength growth curve, and compaction parameters are input into a multi-field coupled damage evolution model to predict crack development and strength degradation during the material's service life. The potential failure threshold output by the damage evolution model is mapped to the initial mix ratio model, and the original mix ratio is corrected based on feedback from risk points.

5. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 4, characterized in that: The construction of the active regulating component auxiliary admixture system includes: Ca(OH)2 slow-release particles are added to the mixture, and alkaline ions are gradually released within the initial 2 to 4 hours to increase the local pH value of the interface area of ​​the recycled aggregate; the mineral powder and hydration products are used to react synergistically to enhance the interfacial activity and promote the formation of gel-like structures; the Ca(OH)2 slow-release particles are coated with organic polymers or inorganic coating materials to control the release rate and duration of alkaline ions.

6. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 5, characterized in that: The mineral powder is added in an amount of 10% to 25% of the total mass of the cementitious material, and is used to participate in the formation reaction of the interface cementitious structure in the early stage of hydration; the auxiliary admixture system is centered on recycled aggregate particles, and is distributed in a directionally distributed manner in the interface area through surface pre-spraying, pre-mixed coating or dry mixing.

7. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 6, characterized in that: The damage evolution model is used in the following steps: S1. Collect the physical and mechanical properties of recycled aggregate, including crushing rate, porosity, and residual glue content; S2. Obtaining interface energy consumption parameters, strength growth curve and construction compaction parameters; S3, inputting the multi-source parameters S1 and S2 into a multi-field coupled damage evolution model, which is used to simulate the crack development and strength degradation process of materials under the effects of traffic loads, dry-wet cycles and temperature changes during long-term service; S4. The model outputs potential failure thresholds, including interface microcrack accumulation values, strength drop rates, and strain concentration area distribution; A mapping relationship is established between the failure threshold and the gradation, water-cement ratio and admixture dosage parameters in the initial mix design.

8. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 7, characterized in that: The damage evolution model is a multi-field joint numerical model of coupled mechanics, moisture migration and temperature effects, which is used to dynamically evaluate the material performance degradation path; the recycled aggregate characteristic parameters include aggregate particle size distribution, mud content, water absorption rate and residual rubber coating thickness.

9. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 8, characterized in that: The interfacial energy dissipation parameters are obtained through micro shear tests or interfacial bonding tests and are used to evaluate the energy dissipation characteristics between aggregate and paste.

10. The method for optimizing the mix ratio of cement-stabilized crushed stone base with recycled aggregate according to claim 9, characterized in that: The damage evolution model identifies potential failure risks by dynamically comparing the design strength with the simulated strength trend curve, and marking the crack leader area and the interface weak zone; the proportion correction includes optimizing the fine aggregate ratio, cement content or interface reinforcement components.

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