Phosphorus slag ceramsite lightweight thermal insulation concrete structure preparation control system and method

Abstract

The invention discloses a phosphorus slag ceramsite lightweight thermal insulation concrete structure preparation control system and method, and particularly relates to the technical field of building material preparation, and the method comprises the following steps: collecting ceramsite physical information, establishing a multi-dimensional parameter set, and setting a component mapping rule; ceramsite space configuration is completed according to mapping; the prepared ceramsite is subjected to surface pretreatment and mixed to form preformed concrete, and the preformed concrete is cured after performance evaluation; maintenance is controlled according to a multi-section temperature and humidity linkage path, and whether the next section is entered or not is judged by the trained machine learning model; when the conditions are not met, the control conditions are updated through optimization steps, and judgment is executed again until all the curing processes are completed; according to the invention, the performance matching configuration of the phosphorus slag ceramsite in the structure is realized, and the preformed concrete is maintained after the performance of the preformed concrete reaches the standard; and through path analysis and state judgment, the multi-section curing process is dynamically regulated and controlled, and the performance consistency and control precision of the concrete structure are improved.

Description

Technical Field

[0001] This invention relates to the field of building material preparation technology, and more specifically, to a control system and method for preparing lightweight thermal insulation concrete structures using phosphorus slag ceramsite. Background Technology

[0002] Lightweight expanded clay concrete, as a novel composite material that combines structural load-bearing capacity with thermal insulation properties, is widely used in green buildings, prefabricated components, and energy-saving exterior wall systems. Among them, phosphorus slag expanded clay, made from industrial solid waste phosphogypsum or phosphorus slag and expanded into a porous structure, not only demonstrates significant resource utilization but also possesses low density, good thermal insulation, and certain mechanical strength, making it a key environmentally friendly lightweight aggregate that has been the focus of research and promotion in recent years.

[0003] However, existing concrete preparation processes based on phosphorus slag ceramsite mainly rely on conventional aggregate screening and uniform mixing. This fails to achieve differentiated alignment of the physical properties and functional areas of the ceramsite within the structural components, resulting in disordered distribution of the ceramsite in the pressure-bearing zone, insulation zone, and connection zone. Consequently, inconsistencies arise between load-bearing capacity, interfacial bond strength, and overall thermal performance, severely restricting the overall balance of structural performance.

[0004] Furthermore, traditional concrete curing path designs mostly employ fixed temperature and humidity gradient settings, lacking a judgment mechanism linked to the actual reaction state of the material. Especially in multifunctional composite structures, due to the complex interface structure between ceramsite and cement paste, failure to accurately assess the hydration reaction intensity and interfacial stress state will lead to the continuous accumulation of early microstructural disturbances, inducing problems such as interface cracking, delamination, or permeability degradation during subsequent loading or service. Regarding control strategies, existing methods often rely on experience to determine the rate of temperature and humidity change and curing time windows, ignoring the material's own state feedback signals and lacking data-driven adaptive control capabilities. Once insufficient curing reaction, uneven local moisture absorption, or decreased adhesion occur, it is difficult to identify and adjust control parameters in a timely manner, ultimately affecting the overall concrete performance stability and construction consistency. Therefore, this paper proposes a control system and method for preparing lightweight thermal insulation concrete structures using phosphorus slag ceramsite to address the aforementioned problems. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for controlling the preparation of lightweight thermal insulation concrete structures using phosphorus slag ceramsite includes the following steps:

[0007] The first step is to collect physical information of phosphorus slag ceramsite, establish a multi-dimensional parameter set representing different physical performance parameter ranges, and pre-set the mapping rules between ceramsite performance and component regions according to the type of structural component.

[0008] The second step is to use each set of performance parameters of the expanded clay as input and assign them to the corresponding areas in the structural components through mapping rules to complete the spatial configuration of the expanded clay in different structural areas.

[0009] The third step is to pretreat the surface of the expanded clay aggregate in the completed area and mix it with cement paste to form pre-formed concrete. Then, the performance of the pre-formed concrete is evaluated. If the preset performance conditions are met, the subsequent curing steps are carried out.

[0010] The fourth step involves controlling the curing process using a multi-segment temperature and humidity linkage path. Path analysis is performed within each curing cycle to obtain the hydration reaction index and interface stress index. These indices are then input into the trained machine learning model for state judgment. When the judgment result meets the transition conditions for the current segment, the process proceeds to the next segment. If the requirements are not met, the optimization step is initiated, and the updated curing control conditions are applied to the current segment's control process. Afterward, parameters are re-collected, and state judgment is executed again, ultimately resulting in a concrete assembly with the multi-segment temperature and humidity linkage path completed.

[0011] In a preferred embodiment, during the process of establishing a multidimensional parameter set representing different physical performance parameter ranges, the collected particle density, compressive strength, and moisture absorption rate data are normalized, and segmented according to the normalization results using an equidistant interval division method. Initial weight coefficients are then assigned as density q1, compressive strength q2, and moisture absorption rate q3, respectively, satisfying q1+q2+q3=1. Then, the parameter combinations are clustered and grouped using weighted Euclidean distance, and each group is used as a component parameter input entry. This entry is directly used for the allocation and judgment of structural component regions in the subsequent mapping process.

[0012] In a preferred embodiment, when setting the mapping rules between the properties of expanded clay aggregate and the component region, parameter matching is completed using a judgment method composed of a rule set, which includes three basic rules:

[0013] Firstly, if the density of the expanded clay particles is between interval a and b, and the corresponding compressive strength value is not less than the set critical compressive strength value, then it is classified into the first type of component area.

[0014] Secondly, if the moisture absorption rate of the particles is less than the humidity boundary value and the density is between c and d, then they are classified into the second type of component area.

[0015] Third, if the above two rules are not met, the system enters the intermediate component area. The three rules are executed in the form of IF-THEN logic. The judgment benchmark parameters are provided by the values ​​of each dimension in the parameter set group. During the execution process, each match is only assigned to one type of component area, and there is no cross-assignment.

[0016] In a preferred embodiment, during the process of allocating ceramsite to corresponding areas in structural components, a mapping relationship is established between each component parameter input entry of ceramsite and the location of the building structure. This mapping relationship is based on component area classification rules. The output first-class component area, second-class component area, and third-class component area correspond to the pressure-bearing area, non-pressure-bearing area, and connection transition area marked in the building structure drawing, respectively, and a structural area configuration table is generated. Each entry in the structural area configuration table maintains a unique association with its assigned location.

[0017] In a preferred embodiment, the performance evaluation of precast concrete is conducted using a dual-index evaluation method, which includes using interfacial bond strength and overall density as two independent evaluation values, while also incorporating the following evaluation expression:

[0018] Divide the interfacial bonding strength by the interfacial thickness to obtain the bonding coefficient per unit area. Multiply the overall density value by the average moisture absorption rate of the ceramsite to obtain the moisture absorption density coefficient. When both indicators meet the corresponding value range, it is considered that the preset performance conditions are met. If at most one indicator meets the corresponding value range, it is marked as a state to be adjusted and the curing step is terminated.

[0019] In a preferred embodiment, the calculation process of the hydration reaction index includes the following steps:

[0020] A hydration heat release function is constructed as the first derivative term of the time variable. The convolution structure of the temperature change rate and relative humidity change rate within the current curing cycle is used as a dynamic weighting function. This weighting function is applied to the hydration heat release function and a weighted integral is performed. The resulting integral value is normalized to the weighted release intensity per unit time. This release intensity is then modified by the standard deviation of temperature and humidity fluctuations within the cycle to form a modulation factor. The modified result is defined as the hydration reaction index for that cycle.

[0021] In a preferred embodiment, the calculation process of the interface stress index includes the following steps:

[0022] The micro-strain response time series of the interface between ceramsite and cement paste was collected during the current curing cycle. A strain change sequence was formed using a fixed sampling frequency. This sequence was then subjected to third-order time-difference processing, and the maximum rate of change was extracted as the strain disturbance index. Subsequently, the humidity change time series was collected during the same cycle. The humidity change spectrum was extracted using Fourier transform, and the power spectral density value of this spectrum in the mid-frequency range was calculated as the environmental disturbance index. The strain disturbance index and the environmental disturbance index were input into a cross-modulation function. This function adopts a bivariate nonlinear product form, defined as the product of the strain disturbance index and the environmental disturbance index divided by the square root of their squares, and then multiplied by the logarithm of the number of sampling points in the cycle. The calculated result is defined as the interface stress index.

[0023] In a preferred embodiment, when the hydration reaction index and the interface stress index are input into the trained machine learning model for state judgment, the machine learning model is a binary classification model built based on supervised learning. The input is a two-dimensional state vector composed of the hydration reaction index and the interface stress index collected and calculated in each curing cycle, and the output is the segment state category, with a category value of 0 or 1, where 0 indicates that the transition condition of the current segment has been met, and 1 indicates that the transition condition of the current segment has not been met.

[0024] In a preferred embodiment, when entering the optimization step, the hydration reaction index, interfacial stress index, temperature change rate, humidity change rate, and curing time of the current segment are input together into the fuzzy logic unit. The fuzzy logic unit outputs the updated curing control conditions, which include the new temperature change rate, humidity change rate, and the time window length that needs to be extended for the current segment.

[0025] In a preferred embodiment, a control system for preparing lightweight thermal insulation concrete structures using phosphorus slag ceramsite specifically includes:

[0026] The parameter acquisition module is used to collect physical information of phosphorus slag ceramsite, establish a multi-dimensional parameter set representing different physical performance parameter ranges, and preset the mapping rules between ceramsite performance and component regions according to the type of structural component.

[0027] The location configuration module is used to take each set of performance parameters of the ceramsite as input and assign them to the corresponding areas in the structural components through mapping rules, thereby completing the spatial location configuration of the ceramsite in different structural areas.

[0028] The preforming evaluation module is used to pre-treat the surface of the expanded clay aggregate configured in the completed area and mix it with cement paste to form preformed concrete. Then, the performance of the preformed concrete is evaluated. If the preset performance conditions are met, the subsequent curing steps are carried out.

[0029] The path judgment module is used to control the curing process according to multiple temperature and humidity linkage paths, and to perform path analysis within each curing cycle to obtain the hydration reaction index and the interface stress index. The hydration reaction index and the interface stress index are then input into the trained machine learning model for state judgment. When the judgment result meets the transition conditions of the current segment, the control moves to the next segment.

[0030] The control optimization module is used to execute optimization steps when the judgment result does not meet the transfer conditions of the current segment, and apply the updated maintenance control conditions to the control process of the current segment. Then, the parameters are collected again and the status judgment is executed again, and finally the concrete assembly with multiple temperature and humidity linkage paths completed is obtained.

[0031] The technical effects and advantages of this invention are as follows:

[0032] This invention achieves precise configuration of phosphorus slag ceramsite in concrete structures based on its physical properties by establishing a multi-dimensional parameter set and combining it with mapping rules. By collecting the physical information of phosphorus slag ceramsite and constructing a multi-dimensional parameter set reflecting different ranges of physical performance parameters, this invention can systematically express the specific performance characteristics of the ceramsite. Furthermore, by combining the mapping rules preset for the structural component type, each set of ceramsite performance parameters can be clearly mapped to different regions in the concrete structural component, thereby achieving a balance between the rationality and functionality of spatial configuration. This closed-loop allocation mechanism from data acquisition to structural configuration enhances the coordination of concrete in terms of physical function and mechanical response, effectively improving the reliability of the overall structural performance and the efficiency of material use.

[0033] This invention employs a performance evaluation and screening mechanism to ensure that precast concrete entering the curing stage meets key performance conditions. After the spatial arrangement of the expanded clay aggregate is completed, the invention pre-treats the surface of the aggregate and mixes it with cement paste to produce precast concrete. A clear performance evaluation process is then established to determine whether preset performance conditions are met; only concrete meeting these standards proceeds to the subsequent curing steps. This approach prevents substandard concrete from directly entering the curing process, ensuring the fundamental performance of the formed structure from the outset. It also provides a stable input basis for subsequent temperature and humidity-controlled curing and enhances the connection quality between different stages of the forming process.

[0034] This invention achieves phased dynamic control of the concrete curing process through path analysis and state judgment mechanisms. During curing, the invention executes control operations according to a multi-segment temperature and humidity linkage path. Path analysis is performed within each curing cycle, and the hydration reaction index and interfacial stress index are calculated as quantitative expressions of the current segment's state. These two indicators are then input into a trained machine learning model for judgment, determining whether to proceed to the next curing segment. If the transition conditions are not met, an optimization step is initiated, and the judgment loop is executed again using updated curing control conditions until the entire path process is completed. This judgment mechanism based on phased state feedback improves the adaptability and intelligence of the curing process, ensures quality consistency between different segments, and ultimately forms a concrete assembly with a complete control path and dynamically controllable responses. Attached Figure Description

[0035] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0036] Figure 1 This is a schematic diagram of the preparation and control method of a lightweight thermal insulation concrete structure made of phosphorus slag ceramsite according to the present invention.

[0037] Figure 2 This is a schematic diagram of the control system for the preparation of a lightweight thermal insulation concrete structure using phosphorus slag ceramsite, as described in this invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Reference Figure 1 - Figure 2 The following examples were obtained:

[0040] Example 1: A method for controlling the preparation of lightweight thermal insulation concrete structures using phosphorus slag ceramsite, comprising the following steps:

[0041] The first step involves collecting physical information on phosphorus slag ceramsite, establishing a multi-dimensional parameter set representing different physical performance parameter ranges, and pre-setting mapping rules between ceramsite performance and component regions based on the type of structural component. The core of this step is obtaining key basic data constituting concrete aggregate. As a lightweight granular material with heterogeneous origin, phosphorus slag ceramsite exhibits significant batch-to-batch variations in particle size, density, strength, and hygroscopic behavior. Therefore, standardized physical parameter collection is the primary prerequisite for achieving controllable structural distribution. By collecting and statistically analyzing the physical information of phosphorus slag ceramsite, a parameter set with particle density, compressive strength, and hygroscopicity as the main dimensions is constructed, forming a multi-dimensional vector space to express the distribution range of each batch of ceramsite in terms of actual physical properties. Based on this, combined with the type and functional characteristics of building structural components, a correspondence between ceramsite performance and structural regions is established, and a mapping mechanism is formed through rule setting. This lays the parameter and logical foundation for subsequent material distribution and structural coordination. Furthermore, in practical applications, particle size is generally screened beforehand.

[0042] The second step involves using each set of performance parameters of the expanded clay aggregate as input and assigning them to corresponding regions within the structural components through mapping rules, thus completing the spatial allocation of the expanded clay aggregate in different structural regions. This step, based on the multi-dimensional parameter set and structural region mapping rules established in the previous stage, unfolds the spatial allocation behavior for each type of expanded clay aggregate. Specifically, the physical performance parameter vector of each set of expanded clay aggregate is used as input and imported into the mapping mechanism for rule judgment, thereby determining the target region affiliation of that type of expanded clay aggregate in the building structural components. This allocation process ensures the spatial coupling of material performance and structural function; that is, high-strength expanded clay aggregate is allocated to high-load-bearing capacity areas, low-hygroscopic expanded clay aggregate is allocated to the outer thermal insulation areas, and parameter-neutral expanded clay aggregate is allocated to the intermediate transition areas, thereby forming a spatial structural structure with layer-by-layer matching in physical performance. This allocation behavior not only improves the load rationality and thermal performance of the overall structure but also provides accurate spatial location information for subsequent material mixing and interface treatment.

[0043] The third step involves surface pretreatment of the expanded clay aggregates allocated in the designated areas, followed by mixing with cement paste to form pre-formed concrete. The pre-formed concrete then undergoes performance evaluation. If it meets the preset performance conditions, it proceeds to the subsequent curing steps. This step facilitates the transition from single-material configuration to a multi-phase composite structure. First, after spatial allocation, the surface of the expanded clay aggregates undergoes pretreatment. This process aims to improve the interfacial compatibility and bonding strength between the aggregates and the cement paste, enhancing the structural integrity and stability of the composite material in the early stages of hardening. Subsequently, the treated expanded clay aggregates and cement paste are uniformly mixed according to a set ratio to obtain pre-formed concrete, serving as a semi-finished product before performance evaluation. At this stage, the pre-formed concrete undergoes a dual-index performance evaluation, relying on interfacial bond strength and overall density as two independent dimensions as judgment criteria. The evaluation uses corresponding calculation expressions to determine whether it meets the predetermined performance requirements. This evaluation determines whether this batch of concrete can enter the curing process, ensuring that the components entering the curing stage possess the basic performance required for structural design in their initial state.

[0044] The fourth step involves controlling the curing process using a multi-segment temperature and humidity linkage path. Path analysis is performed within each curing cycle to obtain the hydration reaction index and interfacial stress index. These indices are then input into a trained machine learning model for state judgment. When the judgment result meets the transition conditions for the current segment, the process proceeds to the next segment. If the requirements are not met, an optimization step is initiated, updating the curing control conditions and applying them to the current segment's control flow. Parameters are then re-collected, and state judgment is executed again, ultimately resulting in a concrete assembly with all multi-segment temperature and humidity linkage paths completed. This step constitutes the core control logic of the curing control stage, demonstrating the intelligent feedback mechanism of this method in multi-cycle response adjustment. In actual curing, a multi-segment temperature and humidity linkage path is used for time-series control of the precast concrete. Each curing cycle acts as an independent control unit, performing path analysis based on collected environmental changes and material reaction data to generate the hydration reaction index and interfacial stress index for the current stage. This binary state vector is input into the trained machine learning model for classification judgment. Based on the output state category, it is determined whether the current cycle meets the conditions for entering the next curing cycle. If the model determines that the current state does not yet meet the transition requirements, it outputs new curing control conditions based on existing reaction data and current environmental parameters through a fuzzy logic mechanism. These conditions include the rate of change of temperature and humidity and the required extended time period. The model then adjusts the control parameters for the current segment accordingly and executes the judgment process again. This constitutes a closed-loop control mechanism of "judgment—optimization—feedback—rejudgment," ensuring that each curing response is sufficiently stable and ultimately achieving the formation of a complete multi-segment temperature and humidity linkage path controlled concrete assembly.

[0045] In establishing a multidimensional parameter set representing different ranges of physical performance parameters, the first step is to process the physical information of the collected phosphorus slag ceramsite particles. Here, "physical information" refers to three representative basic physical performance indicators: particle density, compressive strength, and moisture absorption rate. Specifically: particle density refers to the mass of ceramsite particles per unit volume, reflecting its structural compactness; compressive strength refers to the ability of ceramsite to resist axial compressive loads, a key indicator of load-bearing performance; and moisture absorption rate indicates the ability of ceramsite to absorb moisture under certain humidity conditions, significantly affecting the material's thermal insulation and interfacial hydration characteristics.

[0046] Because the dimensions and numerical ranges of different physical parameters vary significantly, directly using them for joint judgment may lead to bias. Therefore, normalization processing needs to be performed on the three types of parameters separately. Normalization processing refers to converting the original values ​​into a uniform dimensionless interval (usually [0,1]), so that different parameters can be compared at the same order of magnitude, thereby improving the balance of cluster judgment. A specific method can be to use a maximum-minimum linear mapping method, for example, subtracting the minimum density in the sample set from the density value of each sample and then dividing by the range to obtain its normalized density value.

[0047] After normalization, the normalized density, compressive strength and moisture absorption data are segmented using the equidistant interval division method. That is, each parameter is divided into several segments in the [0,1] interval. The number of segments can be preset according to the sample size or performance requirements (e.g., 5, 7 or 9 segments). Each segment represents a performance level interval of the parameter.

[0048] Subsequently, initial weight coefficients are set to reflect the importance of the three parameters to the performance matching of structural components. Density, compressive strength, and moisture absorption rate are assigned weights q1, q2, and q3 respectively, satisfying the constraint: q1 + q2 + q3 = 1. This constraint ensures the normalization of the overall weights, making the influence of different parameters on the final clustering result clearly controllable at the numerical level. For example, in one setting, q1 = 0.3, q2 = 0.5, and q3 = 0.2 can be chosen to emphasize the priority of compressive strength in load-bearing components. Next, weighted Euclidean distance is used as a similarity measure between parameter combinations to cluster and group the normalized and segmented weighted sample data. Weighted Euclidean distance refers to the square root of the weighted sum of the squared differences of each pair of ceramsite samples across the three parameter dimensions. This clustering method can be performed using K-means or hierarchical clustering, and each group of ceramsite obtained from the final clustering is uniformly represented as a component parameter input entry. An entry refers to a vector of typical performance combinations represented by a cluster center point, which serves as the basis for decision-making in the allocation and mapping of material properties in structural regions.

[0049] Furthermore, it should be noted that in the first step of this invention, the objective is to "establish a multi-dimensional parameter set representing different physical performance parameter ranges" for component area configuration. Prior to this step, the ceramsite raw material has typically been processed according to particle size ranges using physical methods (such as standard sieving). For example, before the feeding process, all particles smaller than 5mm or larger than 20mm will be sieved out, retaining only particles that meet the particle size requirements of the structural components. Therefore, particle size no longer possesses "differentiation" or "discriminative power," and no longer has distinguishing effectiveness in clustering and grouping. It does not need to be included in the parameter set. In subsequent steps, each component parameter input entry will serve as an independent allocation judgment unit, directly entering the matching process of "pre-setting the mapping rules between ceramsite performance and component areas based on the structural component type." This determines which target area of ​​the building structural component, such as load-bearing, non-load-bearing, or transitional areas, should this type of ceramsite be allocated to. Each entry belongs to only one type of structural area, ensuring clear classification boundaries and unique material deployment.

[0050] When setting the mapping rules between the properties of expanded clay aggregate and the component regions, a judgment method based on a rule set is used to complete parameter matching. Here, "mapping rules" refer to the correspondence between the physical performance parameters of materials and the functional requirements of structural components, used to guide the rational allocation and spatial deployment of materials; "rule set" refers to a set of judgment logic expressed in formal condition-result statements, used to execute the parameter matching process. This rule set includes three basic rules, each expressed in IF-THEN logic form, i.e., "if a certain set of specific conditions is met, then perform a certain classification operation." Each rule uses the grouping data of the expanded clay aggregate physical parameter dimension as the judgment benchmark to ensure that the matching process is executable and non-overlapping.

[0051] Rule 1: If the density of the expanded clay aggregate is between interval a and b, and the corresponding compressive strength value is not less than the set critical bearing value, it is classified into the first category of structural components. In this rule: particle density represents the mass of expanded clay aggregate per unit volume, reflecting the density of its internal structure; compressive strength represents the ultimate stress that expanded clay aggregate can withstand under pressure, which is a key indicator for evaluating its load-bearing performance; the critical bearing value refers to the minimum strength requirement under a preset structural load match, which can be derived from the engineering safety factor and the design load; the interval [a, b] can be a segmented range of normalized density values ​​between 0.65 and 0.85; expanded clay aggregate that meets this condition is classified into the first category of structural components, which usually refers to the core bearing parts of a building structure that bear the main vertical or horizontal loads, such as column cores and shear wall bases.

[0052] Rule 2: If the moisture absorption rate of the particles is less than the humidity boundary value and the density is between c and d, then they are classified into the second type of component area. This rule is used to identify a group of expanded clay particles with low water absorption and moderate density: the moisture absorption rate represents the moisture adsorption capacity of expanded clay particles under a certain relative humidity. Low moisture absorption helps to improve thermal insulation performance and durability; the humidity boundary value is the maximum allowable moisture absorption ratio under the design environment, such as 8%; the interval [c, d] can be the normalized density value between 0.40 and 0.60, representing the lightweight and stable zone; expanded clay particles that meet this condition are classified into the second type of component area, and are usually used for thermal insulation or non-load-bearing areas on the periphery of the structure, such as the core layer of floor slabs or the filling strip of the enclosure.

[0053] Rule 3: If the above two rules are not met, then the component enters the intermediate component area. This is a catch-all rule, meaning that combinations of expanded clay aggregate parameters that cannot be clearly classified into the first two categories will be classified into the third component area. This area is usually used for connections, transitions, or buffer zones between components, such as beam-column joint areas, vertical transition layers, etc.

[0054] During the execution of the three rules, each set of ceramsite parameters only matches one rule, and is assigned to only one type of component area in each matching process. There is no cross-assignment, thus ensuring the single-valuedness of the assignment judgment and the uniqueness of the mapping logic.

[0055] In the process of allocating expanded clay aggregates to corresponding areas within structural components, a mapping relationship is established between each component parameter input entry and its location within the building structure. Specifically: the component parameter input entry refers to the expanded clay aggregate performance combination identifier generated by the aforementioned clustering method, representing a typical group of expanded clay aggregates; the building structure location refers to the spatial location area in the component diagram, including the pressure-bearing core area, the non-pressure-bearing peripheral area, and the connection buffer zone, etc. This mapping relationship is generated based on the output results of the aforementioned component area classification rules. In this step, the output of the mapping relationship, namely the first type of component area, the second type of component area, and the third type of component area, corresponds to the pressure-bearing area, the non-pressure-bearing area, and the connection transition area marked in the building structure diagram, respectively. This matching relationship is one-to-one, with no overlap or omissions. To ensure deployment consistency, the above mapping results are compiled into a structural area configuration table. This table lists each group of expanded clay aggregate component parameters and their corresponding specific structural area in the form of entries. Each entry maintains a unique association with its assigned structural location; that is, one entry belongs to only one location, and serves as the input for subsequent mixing and structural configuration instructions.

[0056] For example, a group of expanded clay aggregates has a normalized density of 0.75, a compressive strength of 18 MPa, and a moisture absorption rate of 6%. Setting a=0.70, b=0.85, and a critical compressive strength of 15 MPa, it meets the first rule and is classified into the first type of component area. In the building structure drawing, this area corresponds to the bottom of the shear wall, and the entry in the configuration table is bound to its spatial location for subsequent deployment. Another group of expanded clay aggregates has a density of 0.55 and a moisture absorption rate of 5%, meeting the second rule and corresponding to the outer filling layer. If a third group of expanded clay aggregates does not meet the first two rules, for example, if the density is too high but the strength is insufficient, it is classified into the connecting transition area.

[0057] After the spatial configuration is completed, the expanded clay aggregate undergoes surface pretreatment and is then mixed with cement paste to form precast concrete. This process is a crucial step in the transition from particle size distribution to composite material configuration, directly impacting the structural uniformity and interfacial stability of the final concrete assembly. Surface pretreatment refers to applying interfacial control processes to the spatially configured expanded clay aggregate particles to enhance their physical and chemical affinity with the cement paste. Treatment methods can include: physical treatments, such as using high-pressure air to remove surface dust; chemical treatments, such as impregnation with hydrophilic or coupling agent solutions to impart functional groups or wettability to the particle surface; and surface structure modification, such as micro-roughening to enhance mechanical embedding. The treated expanded clay aggregate particles exhibit higher "interfacial activity," meaning they more readily form a continuous interfacial structure during subsequent bonding with the cement paste. This facilitates the release of interfacial stress and mechanical transition, thereby improving the overall crack resistance and stability of the composite material.

[0058] Cement paste refers to a fluid cementitious material system formed by mixing cement, water, and other optional additives (such as water-reducing agents and retarders) in a certain proportion. In this step, expanded clay aggregate, as a lightweight aggregate, is fully mixed with cement paste at a set mix ratio to form an initial structure. "Mixing and mixing" refers to the process of uniformly distributing expanded clay aggregate and cement paste in a mechanical mixing or rotating device. Its objectives are: to ensure uniform distribution of expanded clay aggregate in the paste, avoiding local accumulation or segregation; to promote the penetration of the paste into the pores and gaps on the surface of the expanded clay aggregate, forming an interface anchoring structure; and to initially form a plastic concrete body with a three-dimensional shape, laying the foundation for subsequent curing and hardening. Precast concrete refers to a preliminary composite material body that has not yet entered the curing or hardening stage, but has completed the placement of expanded clay aggregate and mixing with the paste. Its main characteristics are: the spatial configuration is already in place; the material components have been mixed; and the properties have not yet been cured and stabilized. Precast concrete is usually in a transitional state between fluidity and plasticity, and can be subjected to operations such as molding, compaction, and pouring. In this invention, the precast concrete also needs to undergo subsequent performance evaluation steps to ensure that its initial performance meets the established standards.

[0059] Example Description: Taking an insulated wall as an example, after completing the second step of configuration, the corresponding batch of expanded clay aggregate (density classified as 0.55, moisture absorption rate of 6%, compressive strength of 15 MPa) is assigned to the outer non-pressure-bearing zone. The expanded clay aggregate is impregnated with a solution containing 0.1% surfactant and then dried for 30 minutes to form particles with a hydrophilic surface. A cement paste is then prepared at a mass ratio of 1:0.45, with 1% high-efficiency water-reducing agent added to improve its fluidity. The pretreated expanded clay aggregate is added to the paste at a 50% volume ratio and mixed in a planetary mixer for 5 minutes to generate uniform pre-formed concrete. This concrete exhibits good fluidity and initial setting stability, with a bond strength of 0.9 MPa at the sample stage. Its density index is superior to the benchmark standard, meeting the requirements for subsequent performance evaluation and curing processes.

[0060] When evaluating the performance of precast concrete, a dual-index evaluation method is used. The goal of the performance evaluation is to determine whether the initial-state concrete has reached the physical performance thresholds required for acceptable formal curing control. The dual-index evaluation method involves simultaneously evaluating the concrete using two independent and physically complementary performance parameters to improve the stability and rationality of the evaluation. The two evaluation values ​​used in this method are:

[0061] The first indicator is the interfacial bond strength, which refers to the maximum stress that the surface of the expanded clay aggregate and the cement paste in precast concrete can withstand under tensile conditions. It is a key indicator for evaluating the interfacial bonding quality of composite materials. The interfacial thickness refers to the physical thickness of the transition region between the expanded clay aggregate and the cement paste. This region is composed of penetration, adsorption, and reaction, and is usually between tens and hundreds of micrometers. The unit area bond coefficient is the ratio obtained by dividing the interfacial bond strength by the interfacial thickness. It reflects the interfacial bearing capacity density per unit area, with dimensions of MPa / μm, and has a more direct structural significance. The higher the coefficient, the higher the stress transfer capacity can be achieved in a thinner interfacial layer, reflecting that the interfacial reaction layer is uniform, dense, and free of microcracks.

[0062] The second indicator is: Overall density, which refers to the degree of substantial filling of precast concrete per unit volume, reflecting the structural porosity and material homogeneity; the average moisture absorption rate of expanded clay aggregate is the ability of expanded clay aggregate particles to absorb moisture, and is the arithmetic mean among different expanded clay aggregates; the moisture absorption density coefficient is the result of multiplying the overall density value by the average moisture absorption rate of expanded clay aggregate, and is used to express the responsiveness of material density to moisture migration under moisture-sensitive conditions; if the moisture absorption density coefficient is lower than a certain threshold, it indicates that there are potential void water accumulation zones in the concrete structure, which may lead to subsequent performance degradation or cracking.

[0063] During the performance evaluation process, the above two indicators are calculated first: the interfacial bonding strength (in MPa) is divided by the interfacial thickness (in μm) to obtain the bonding coefficient per unit area; the overall density (dimensionless proportional value, between 0 and 1) is multiplied by the average moisture absorption rate of the ceramsite (%) to obtain the moisture absorption density coefficient.

[0064] When both indicators meet their respective set numerical ranges (e.g., the bond strength per unit area is greater than 0.025 MPa / μm, and the hygroscopic density coefficient is less than 0.42), the precast concrete is considered to meet the basic performance conditions for entering curing control, meaning its interfacial properties are stable and its structural porosity is well controlled. If only one indicator falls within the target range (i.e., "at most one indicator meets the corresponding numerical range"), it indicates that the precast concrete has a unidirectional performance deviation. In this case, to ensure the stability of subsequent curing control, the sample will be marked as "to be adjusted," and the curing process will be terminated, awaiting further mix optimization or re-pretreatment.

[0065] The calculation process for the hydration reaction index includes the following steps:

[0066] A hydration heat release function is constructed as the first derivative term of the time variable. The convolution structure of the temperature change rate and relative humidity change rate within the current curing cycle is used as a dynamic weighting function. This weighting function is applied to the hydration heat release function and a weighted integral is performed. The resulting integral value is normalized to the weighted release intensity per unit time. This release intensity is then modified by the standard deviation of temperature and humidity fluctuations within the cycle to form a modulation factor. The modified result is defined as the hydration reaction index for that cycle.

[0067] Specifically: Step 1, construct the hydration heat release function as the first derivative term of the time variable: The hydration heat release function represents the change in heat released by the hydration reaction over time in a concrete cement-based system, usually denoted as... The unit is joules per gram (J / g) or joules per cubic centimeter (J / cm³), and this function describes the process of heat accumulation during reaction. In this invention, the following is used: As the object of analysis, namely the hydration heat release function with respect to time The first derivative term, representing the rate of change of heat released by the hydration reaction per unit time, is called the instantaneous hydration rate function, denoted as . .

[0068] Step 2: Constructing the Dynamic Weighting Function: This step constructs a dynamic weighting function based on the rate of temperature change and the rate of relative humidity change during the current curing cycle. Definition: The rate of temperature change is... ,in Curing time The ambient temperature and humidity change rate at any given time are: ,in The corresponding time is the curing time. Relative humidity (%); Construct convolutional functions The format is as follows:

[0069] , It is an integral variable, representing the process of calculating from 0 to the current value. Between moments, the product of all past moments' "historical time points" reflects the weighted influence of the temperature and humidity interaction at past time points on the current moment, treating convolution as a kind of "time-weighted review". It's like "sliding" along a timeline back to the past to review each historical moment and observe its impact on current behavior (i.e., hydration rate);

[0070] Step 3, Weighted Integral Calculation: Calculate the hydration rate function from Step 1. With the dynamic weight function in step two Multiply the results and integrate them over the current solidification period t∈[0,Y] to obtain the weighted release total: ,in This represents the weighted total intensity of hydration release over a curing cycle, expressed as an integral value. Divide by the curing cycle time Y to obtain , This is the weighted hydration release intensity per unit time, representing the average performance of hydration intensity under temperature and humidity response modulation.

[0071] Step 4: Construct and correct the modulation factor: Within this period, the standard deviations of the acquired temperature and humidity signals are as follows: Standard deviation of temperature fluctuation Standard deviation of humidity fluctuation Define modulation factor for:

[0072] This factor represents the inhibitory effect of environmental fluctuations on the hydration reaction process. A larger standard deviation indicates more severe fluctuations and a smaller modulation factor. The final definition is the hydration reaction index. for: The result is the hydration reaction index under the current curing cycle, with the unit being normalized hydration intensity. It has dimensionless characteristics, which facilitates periodic comparison and subsequent input into the machine learning model for state judgment.

[0073] The calculation process for the interfacial stress index includes the following steps:

[0074] The micro-strain response time series of the interface between ceramsite and cement paste was collected during the current curing cycle. A strain change sequence was formed using a fixed sampling frequency. This sequence was then subjected to third-order time-difference processing, and the maximum rate of change was extracted as the strain disturbance index. Subsequently, the humidity change time series was collected during the same cycle. The humidity change spectrum was extracted using Fourier transform, and the power spectral density value of this spectrum in the mid-frequency range was calculated as the environmental disturbance index. The strain disturbance index and the environmental disturbance index were input into a cross-modulation function. This function adopts a bivariate nonlinear product form, defined as the product of the strain disturbance index and the environmental disturbance index divided by the square root of their squares, and then multiplied by the logarithm of the number of sampling points in the cycle. The calculated result is defined as the interface stress index.

[0075] Specifically, step U1 involves collecting the micro-strain response time series of the interface between the expanded clay aggregate and the cement paste: During the current curing period, the micro-strain response values ​​of the interface region between the expanded clay aggregate and the cement paste in the precast concrete are collected in real time. Wherein: the micro-strain response value refers to the unit strain caused by relative displacement due to changes in temperature and humidity at the microscale of the interface, which can be collected using distributed fiber optic sensing, micro-resistance strain gauges, or digital image correlation (DIC) methods; the current curing period is defined as the time interval t∈[0,Y], where Y is the duration of this period; sampling is performed at a fixed frequency f to obtain a discrete strain value sequence with equal time intervals. , where n = f * Y.

[0076] Step U2: Third-order time-difference processing and extraction of the maximum rate of change as the strain perturbation index: Third-order time-difference processing is performed on the above micro-strain response sequence to enhance the high-frequency perturbation response. Definition:

[0077] First-order difference ;

[0078] The second difference is divided into ;

[0079] The third difference is divided into ;in, ;

[0080] The third-order difference sequence The rate of change of the strain response reflects the intensity of the response to local disturbances or abrupt changes. The largest absolute value among the third-order differences is extracted as the strain disturbance index. , which serves as the maximum dynamic response characteristic value of the interface stress within this period.

[0081] Step U3: Analyze the spectral distribution of humidity changes and calculate environmental disturbance indicators: Simultaneously collect time series data on humidity changes within this period. , This represents the relative humidity value at time i, with the sampling frequency consistent with the strain data. Perform a Discrete Fourier Transform (DFT) on this sequence:

[0082] Given a time series of length N (i.e., N sampling points), the Discrete Fourier Transform (DFT) transforms it into N complex frequency components, each corresponding to an index value k. Then, the power spectral density of the mid-frequency band is extracted from the frequency domain. Let the mid-frequency interval be... , for example, represents 0.1–0.5 Hz; calculate the average power spectral density. : ; income Defined as an environmental disturbance index, it reflects the degree of mid-frequency energy concentration of humidity fluctuations within a cycle.

[0083] Step U4: Calculate the interface stress index using the cross-modulation function. Input the two indices into the cross-modulation function, as defined below: ; logarithm of sample points This is used to compensate for the scaling effect between different sampling periods. When both perturbation indices are high, the product amplification effect is enhanced; when one is weak, the normalization factor is reduced. Suppress mutations; adjust the logarithmic terms to reasonably adjust the results with sampling density. Final calculated value. Defined as the interfacial stress index of the curing cycle, it is used to quantify the coupled stress state of the interface under disturbance, and the unit is a dimensionless normalized value.

[0084] When the hydration reaction index and the interfacial stress index are input into a trained machine learning model for state determination, this machine learning model is a binary classification model built based on supervised learning. This model is used to determine whether the preset segment transition conditions have been met based on the key reaction state indicators of the concrete within each curing cycle. In this invention, after each curing cycle, the hydration reaction index and the interfacial stress index are calculated by analyzing the data actually collected during that cycle. These two indices respectively characterize the release intensity of the hydration reaction inside the concrete and the fluctuation characteristics of the interfacial stress, both of which have been disclosed in detail in the foregoing content of this specification. The combination of these two indices forms a set of state inputs, constituting the state feature vector of that curing cycle, serving as a quantitative description of the overall behavior of that cycle. This state input is submitted to the pre-trained machine learning model, which internally identifies the category based on the classification boundaries learned during the training phase and outputs the category to which the current state belongs.

[0085] The machine learning model is built using supervised learning, meaning it is trained on a large amount of labeled historical periodic data during model building, enabling the model to learn the segment state classification corresponding to each set of input states. In the training samples, each set of data explicitly includes two input values ​​and an output label representing the segment state. This output label is binary, indicating "meets the current segment transition condition" and "does not meet the current segment transition condition," respectively. The model's final output is a discrete value used to indicate whether the current curing cycle can proceed to the next temperature and humidity control path. If the model's output class value is zero, it means that the hydration and interfacial bonding state of the concrete in the current cycle meets the predetermined physical evolution requirements, and it can directly proceed to the next curing cycle. If the output class value is one, it indicates that the current state has not yet reached the transition threshold, and the current segment state must be maintained. A reassessment is then conducted after adjusting the relevant curing conditions through subsequent optimization steps. This process achieves dynamic judgment and segmented control of each segment state in a multi-segment temperature and humidity linked curing control path, improving the adaptability and consistency of the concrete structure during the forming process. The training process and inference mechanism of the machine learning model are not limited to a specific implementation method and can be implemented using common supervised learning algorithms, including but not limited to decision trees, support vector machines, and neural networks. Through the input structure and judgment logic proposed in this invention, the segment control strategy possesses clear judgment criteria and feedback processes, ensuring that each segment of the maintenance process has precise dynamic controllability.

[0086] In the multi-segment temperature and humidity linkage curing path control method proposed in this invention, to achieve dynamic adjustment and fine control of curing cycle segments that do not meet the transfer conditions, a fuzzy logic device is introduced as the core reasoning and adjustment mechanism when the system enters the optimization step. This fuzzy logic device is used to make a comprehensive judgment based on multiple key state indicators when the current segment's curing state does not meet the set transfer conditions, and outputs updated curing control parameters to achieve adaptive optimization of the current segment's control conditions. In this invention, the fuzzy logic device is designed to receive five input information items from the current segment and output three curing control parameters according to the set fuzzy rule set, forming a structured input-output mapping. Specifically, the input of the fuzzy logic unit includes the following five variables: hydration reaction index: used to characterize the intensity of hydration reaction release in the current segment, indirectly reflecting the matching state between the temperature and humidity environment and the gelling activity; interface stress index: used to characterize the degree of disturbance response of the ceramsite-slurry interface in this segment, reflecting the impact of environmental changes on the integrity of the microstructure interface; temperature change rate: refers to the magnitude of the temperature control gradient change currently executed in this segment, in degrees Celsius per minute; humidity change rate: refers to the humidity control adjustment rate currently executed in this segment, in percentage relative humidity per minute; current segment curing time: records the duration of this segment since its start, in minutes.

[0087] The fuzzy logic unit internally pre-defines multiple fuzzy membership functions, mapping the aforementioned input variables to fuzzy linguistic sets. For example, the hydration reaction index is categorized as "low," "medium," and "high"; the temperature change rate is categorized as "gradual," "moderate," and "drastic"; and the time duration is categorized as "insufficient," "reasonable," and "excessive," etc., entering the fuzzy rule judgment stage as linguistic variables. Subsequently, the fuzzy logic unit calls its rule base, which is a set of IF-THEN type statements constructed based on empirical knowledge and experimental data. For example, if the hydration reaction index is "medium," the interface stress index is "high," and the temperature change rate is "drastic," then the output temperature control suggestion is "reduce," the humidity control suggestion is "maintain," and the delay suggestion is "increase." If the hydration reaction index is "low," the humidity change rate is "gradual," and the curing time is "reasonable," then the output is "increase temperature," "increase humidity," and "shorten delay." By matching the input linguistic variables with the rule base, fuzzy inference output is formed. Finally, the system deblurs each output variable, converting verbal outputs such as "decrease", "increase", and "moderate" into executable numerical values. For example, the new temperature change rate is adjusted to 80% of the original rate; the new humidity change rate is set to a fixed fine-tuning value; and the current segment extension time is 20% of the original planned time.

[0088] The three outputs constitute the new maintenance control conditions, including: a new rate of temperature change, indicating the rate of temperature increase or decrease to be used in the next round of maintenance; a new rate of humidity change, indicating how the response frequency of the humidity control system should be adjusted; and the length of the time window to be extended in the current phase, which is the amount of time added to the original plan to fully complete the reaction process in this phase.

[0089] Example Description: For instance, at the end of a certain curing cycle, the following parameters are collected: hydration reaction index is 0.59; interface stress index is 1287; current temperature change rate is 0.8℃ / min; current humidity change rate is 0.3%RH / min; current curing time is 180 minutes. After being input into the fuzzy logic unit, through fuzzy rule matching and output reasoning, the following outputs are obtained: the new temperature change rate is 0.5℃ / min (reducing the heating rate to prevent amplification of interface disturbances); the new humidity change rate is 0.1%RH / min (stabilizing humidity to alleviate hydration shift); the current time window needs to be extended by 60 minutes; the system will adjust the control process according to the updated curing control conditions and enter the next judgment cycle.

[0090] Example 2: A control system for the preparation of lightweight thermal insulation concrete structures using phosphorus slag ceramsite, specifically including:

[0091] The parameter acquisition module is used to collect physical information of phosphorus slag ceramsite, establish a multi-dimensional parameter set representing different physical performance parameter ranges, and preset the mapping rules between ceramsite performance and component regions according to the type of structural component.

[0092] The location configuration module is used to take each set of performance parameters of the ceramsite as input and assign them to the corresponding areas in the structural components through mapping rules, thereby completing the spatial location configuration of the ceramsite in different structural areas.

[0093] The preforming evaluation module is used to pre-treat the surface of the expanded clay aggregate configured in the completed area and mix it with cement paste to form preformed concrete. Then, the performance of the preformed concrete is evaluated. If the preset performance conditions are met, the subsequent curing steps are carried out.

[0094] The path judgment module is used to control the curing process according to multiple temperature and humidity linkage paths, and to perform path analysis within each curing cycle to obtain the hydration reaction index and the interface stress index. The hydration reaction index and the interface stress index are then input into the trained machine learning model for state judgment. When the judgment result meets the transition conditions of the current segment, the control moves to the next segment.

[0095] The control optimization module is used to execute optimization steps when the judgment result does not meet the transfer conditions of the current segment, and apply the updated maintenance control conditions to the control process of the current segment. Then, the parameters are collected again and the status judgment is executed again, and finally the concrete assembly with multiple temperature and humidity linkage paths completed is obtained.

[0096] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0097] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0098] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0099] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0100] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling the preparation of lightweight thermal insulation concrete structures using phosphorus slag ceramsite, characterized in that, Includes the following steps: The first step is to collect physical information of phosphorus slag ceramsite, establish a multi-dimensional parameter set representing different physical performance parameter ranges, and pre-set the mapping rules between ceramsite performance and component regions according to the type of structural component. The second step is to use each set of performance parameters of the expanded clay as input and assign them to the corresponding areas in the structural components through mapping rules to complete the spatial configuration of the expanded clay in different structural areas. The third step is to pretreat the surface of the expanded clay aggregate in the completed area and mix it with cement paste to form pre-formed concrete. Then, the performance of the pre-formed concrete is evaluated. If the preset performance conditions are met, the subsequent curing steps are carried out. The fourth step involves controlling the curing process using a multi-segment temperature and humidity linkage path. Path analysis is performed within each curing cycle to obtain the hydration reaction index and interface stress index. These indices are then input into the trained machine learning model for state judgment. When the judgment result meets the transition conditions for the current segment, the process proceeds to the next segment. If the requirements are not met, the optimization step is initiated, and the updated curing control conditions are applied to the current segment's control process. Afterward, parameters are re-collected, and state judgment is executed again, ultimately resulting in a concrete assembly with the multi-segment temperature and humidity linkage path completed.

2. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 1, characterized in that, In the process of establishing a multidimensional parameter set representing different physical performance parameter ranges, the collected particle density, compressive strength, and moisture absorption rate data are normalized. Based on the normalization results, the data are segmented using an equidistant interval division method. Initial weight coefficients are assigned as density q1, compressive strength q2, and moisture absorption rate q3, respectively, satisfying q1+q2+q3=1. Then, the parameter combinations are clustered and grouped using weighted Euclidean distance. Each group is used as a component parameter input entry, which is directly used for the allocation and judgment of structural component regions in the subsequent mapping process.

3. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 2, characterized in that, When setting the mapping rules between the properties of expanded clay aggregate and the component area, parameter matching is completed using a judgment method composed of a rule set, which includes three basic rules: Firstly, if the density of the expanded clay particles is between interval a and b, and the corresponding compressive strength value is not less than the set critical compressive strength value, then it is classified into the first type of component area. Secondly, if the moisture absorption rate of the particles is less than the humidity boundary value and the density is between c and d, then they are classified into the second type of component area. Third, if the above two rules do not hold, then proceed to the intermediate component area.

4. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 3, characterized in that, In the process of allocating expanded clay aggregate to the corresponding areas of structural components, a mapping relationship is established between each component parameter input item of expanded clay aggregate and the location of the building structure. This mapping relationship is based on the component area classification rules. The output first type component area, second type component area, and third type component area correspond to the pressure-bearing area, non-pressure-bearing area, and connection transition area marked in the building structure drawing, respectively, and a structural area configuration table is generated. Each item in the structural area configuration table maintains a unique association with its assigned location.

5. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 4, characterized in that, When evaluating the performance of precast concrete, a dual-index evaluation method is used, which includes using interfacial bond strength and overall density as two independent evaluation values, and also introduces the following evaluation expression: Divide the interfacial bonding strength by the interfacial thickness to obtain the bonding coefficient per unit area. Multiply the overall density value by the average moisture absorption rate of the ceramsite to obtain the moisture absorption density coefficient. When both indicators meet the corresponding numerical range, they are considered to meet the preset performance conditions. If at most one indicator meets the corresponding value range, it is marked as pending adjustment and the maintenance process is terminated.

6. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 5, characterized in that, The calculation process for the hydration reaction index includes the following steps: A hydration heat release function is constructed as the first derivative term of the time variable. The convolution structure of the temperature change rate and relative humidity change rate within the current curing cycle is used as a dynamic weighting function. This weighting function is applied to the hydration heat release function and a weighted integral is performed. The resulting integral value is normalized to the weighted release intensity per unit time. This release intensity is then modified by the standard deviation of temperature and humidity fluctuations within the cycle to form a modulation factor. The modified result is defined as the hydration reaction index for that cycle.

7. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 6, characterized in that, The calculation process for the interfacial stress index includes the following steps: The micro-strain response time series of the interface between ceramsite and cement paste was collected during the current curing cycle. A strain change sequence was formed using a fixed sampling frequency. This sequence was then subjected to third-order time-difference processing, and the maximum rate of change was extracted as the strain disturbance index. Subsequently, the humidity change time series was collected during the same cycle. The humidity change spectrum was extracted using Fourier transform, and the power spectral density value of this spectrum in the mid-frequency range was calculated as the environmental disturbance index. The strain disturbance index and the environmental disturbance index were input into a cross-modulation function. This function adopts a bivariate nonlinear product form, defined as the product of the strain disturbance index and the environmental disturbance index divided by the square root of their squares, and then multiplied by the logarithm of the number of sampling points in the cycle. The calculated result is defined as the interface stress index.

8. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 7, characterized in that, When the hydration reaction index and the interface stress index are input into the trained machine learning model for state judgment, the machine learning model is a binary classification model built based on supervised learning. The input is a two-dimensional state vector composed of the hydration reaction index and the interface stress index collected and calculated in each curing cycle. The output is the segment state category, with a category value of 0 or 1, where 0 indicates that the transition condition of the current segment has been met, and 1 indicates that the transition condition of the current segment has not been met.

9. The method for controlling the preparation of lightweight thermal insulation concrete structure using phosphorus slag ceramsite according to claim 8, characterized in that, When entering the optimization step, the hydration reaction index, interfacial stress index, temperature change rate, humidity change rate, and curing time of the current segment are input into the fuzzy logic unit. The fuzzy logic unit outputs the updated curing control conditions, which include the new temperature change rate, humidity change rate, and the time window length that needs to be extended for the current segment.

10. A control system for the preparation of lightweight thermal insulation concrete structures using phosphorus slag ceramsite, based on the method for controlling the preparation of lightweight thermal insulation concrete structures using phosphorus slag ceramsite as described in any one of claims 1-9, characterized in that, Specifically, it includes: The parameter acquisition module is used to collect physical information of phosphorus slag ceramsite, establish a multi-dimensional parameter set representing different physical performance parameter ranges, and preset the mapping rules between ceramsite performance and component regions according to the type of structural component. The location configuration module is used to take each set of performance parameters of the ceramsite as input and assign them to the corresponding areas in the structural components through mapping rules, thereby completing the spatial location configuration of the ceramsite in different structural areas. The preforming evaluation module is used to pre-treat the surface of the expanded clay aggregate configured in the completed area and mix it with cement paste to form preformed concrete. Then, the performance of the preformed concrete is evaluated. If the preset performance conditions are met, the subsequent curing steps are carried out. The path judgment module is used to control the curing process according to multiple temperature and humidity linkage paths, and to perform path analysis within each curing cycle to obtain the hydration reaction index and the interface stress index. The hydration reaction index and the interface stress index are then input into the trained machine learning model for state judgment. When the judgment result meets the transition conditions of the current segment, the control moves to the next segment. The control optimization module is used to execute optimization steps when the judgment result does not meet the transfer conditions of the current segment, and apply the updated maintenance control conditions to the control process of the current segment. Then, the parameters are collected again and the status judgment is executed again, and finally the concrete assembly with multiple temperature and humidity linkage paths completed is obtained.

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