Selection and construction method of high-salt-content soft soil curing agent

Abstract

The invention discloses a high-salt-content soft soil curing agent selection and construction method. The method comprises the following steps: firstly, extracting key characteristic parameters of a soil body through field investigation and sampling, and constructing a soil body characteristic database; primarily screening the curing agents based on feature matching to obtain candidate curing agents; then, building a multi-objective optimization model by taking engineering performance, economic cost, resource consumption and construction feasibility as a core, and solving to obtain an optimal curing agent and a reference ratio thereof; designing a refined mixing amount gradient experiment by taking the reference ratio of the optimal curing agent as the center, and drawing a relation curve of key performance indexes along with the change of the mixing amount to obtain the optimal mixing amount; finally, key characteristic parameters of a construction soil body are obtained in real time, the multi-target optimization model is dynamically called or finely adjusted according to construction real-time data, the multi-target optimization model is solved, and online dynamic adjustment of the mixing amount of the curing agent and technological parameters is achieved. The problems of curing agent model selection, proportioning optimization and construction quality control in a multi-salt coexistence environment are solved.

Description

Technical Field

[0001] This invention belongs to the field of foundation treatment and soil consolidation technology, specifically relating to a method for selecting and constructing a solidifying agent for high-salt soft soil. It is particularly applicable to the selection of solidifying agent type, determination of dosage, and decision-making and optimization of construction quality control in the treatment of soft soil foundations with high chloride content. Background Technology

[0002] High-salinity soft soil, a special type of soil and rock with a wide distribution and complex properties, is not limited to chloride-dominated coastal environments but is also prevalent in inland saline soil areas. The types of salts exhibit significant regional characteristics. Common soluble salts mainly include: chloride salts (such as NaCl, MgCl2, and CaCl2, which are highly hygroscopic, have low freezing points, and are highly corrosive), sulfates (such as Na2SO4 and MgSO4, which easily cause crystallization expansion and corrosion), carbonates / bicarbonates (such as Na2CO3 and NaHCO3, which affect pH and lead to soil dispersion), and nitrates. These salts exist in different proportions and forms on the pore water and particle surfaces of the soil, profoundly affecting the physical and mechanical properties and engineering behavior of soft soil by altering the chemical balance of the soil-water system, the interaction forces between particles, and the cementation methods.

[0003] Therefore, traditional curing methods relying solely on experience or a single strength index are insufficient to address the diverse salt compositions and complex physicochemical effects. For example, sulfate ions react with cement hydration products to form expansive ettringite, leading to cracking of the cured body; magnesium ions may replace calcium ions, damaging the cement stone structure; while high concentrations of chloride salts, although potentially promoting early strength formation, severely exacerbate the risk of steel corrosion and affect the long-term stability of the cured body. Although current curing agent systems (such as cement-based, polymer-based, and various composite curing agents) are abundant, research on the mechanisms specific to certain salt types is insufficient, and the selection process lacks systematic multi-factor decision support. This results in low matching efficiency between curing agent type, dosage, and complex salt-soil systems, frequently causing a series of engineering problems such as uneven reinforcement, poor economic efficiency, and poor durability.

[0004] Furthermore, existing construction technologies severely lack the ability to identify the types, contents, and spatial distribution of salts in soil in real time, failing to provide timely data flow for dynamic design and precise construction. Key processes such as the preparation, addition, mixing, and curing of curing agents largely rely on manual experience and judgment, resulting in strong subjectivity and low standardization in quality control. This leads to significant dispersion in curing effects both vertically and horizontally, making it difficult to guarantee project quality.

[0005] In summary, to solve the complex soft soil foundation treatment problem containing multiple types of soluble salts, it is necessary to construct a more universal, scientific and dynamically optimizable intelligent decision-making method to break through the limitations of single salt types and establish a solidified decision-making framework that covers all common salt types and their interactions. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the technical problem this invention aims to solve is to provide a method for selecting and constructing a solidification agent for high-salt soft soil. The goal is to construct a closed-loop framework from feature recognition to intelligent decision-making and dynamic control, in a scientific, systematic, and adaptable manner, to solve the challenges of solidification agent selection, ratio optimization, and construction quality control in environments with multiple salts, thereby comprehensively improving the reliability, economy, and environmental adaptability of foundation treatment.

[0007] The present invention solves the aforementioned technical problem by adopting the following technical solution: A method for selecting and applying a solidifying agent for high-salt soft soil, characterized by the following steps: Step 1: Extract key soil characteristic parameters through on-site investigation and sampling, and construct a soil characteristic database; Step 2: Initial screening of curing agents based on feature matching to obtain candidate curing agents; Step 3: Construct a multi-objective optimization model with engineering performance, economic cost, resource consumption and construction feasibility as the core, solve the multi-objective optimization model to obtain the optimal curing agent and its benchmark ratio; Step 4: Based on the baseline ratio of the optimal curing agent, refine the mixing ratio; Centered on the baseline ratio of the optimal curing agent, a refined dosage gradient experiment was designed, and the relationship curves of key performance indicators with dosage were plotted to obtain the optimal dosage. Step 5: Dynamically monitor the construction process and dynamically adjust the dosage of curing agent and construction process parameters online; The system acquires key characteristic parameters of the construction soil in real time, dynamically calls or fine-tunes the multi-objective optimization model based on real-time construction data, and solves the multi-objective optimization model to achieve online dynamic adjustment of curing agent dosage and construction process parameters.

[0008] Furthermore, the key characteristic parameters of the soil include physical indicators, chemical salt content parameters, pH value, and organic matter content.

[0009] Compared with the prior art, the beneficial effects of the present invention are: The selection and construction of curing agents have been elevated from discrete, experience-based judgments to a systematic engineering project integrating geochemical analysis, materials science, optimization theory, and intelligent control technology. Its core advantages lie in its systematic nature, adaptability, and forward-looking approach. It can effectively address the challenges of foundation treatment for high-salt soft soils, especially complex soils containing multiple types of harmful salts, and provides a quantifiable and optimizable technical path for achieving efficient, economical, and durable foundation reinforcement. Attached Figure Description

[0010] Figure 1 This is the overall flowchart; Figure 2 A schematic diagram of the Pareto front; Figure 3 This is a graph showing the relationship between strength and curing agent dosage. Detailed Implementation

[0011] Specific embodiments are given below with reference to the accompanying drawings. These specific embodiments are only used to describe the technical solution of the present invention in detail and are not intended to limit the scope of protection of this application.

[0012] like Figure 1 As shown, this invention provides a method for selecting and constructing a solidifying agent for high-salt soft soil. Based on the systematic identification of multi-dimensional soil characteristics, with multi-objective intelligent optimization as the core and dynamic feedback adjustment as a guarantee, it achieves precise decision-making and construction control of the solidifying agent throughout the entire process. The method includes the following steps: Step 1: Extract key soil characteristic parameters through on-site investigation and sampling, and construct a soil characteristic database; Through on-site investigation and indoor testing, key characteristic parameters affecting soil consolidation effects were obtained and quantified. These parameters included not only basic physical indicators (such as natural moisture content, particle size distribution, and plasticity index), but also, more importantly, a complete chemical salt profile (including but not limited to the content and ionic composition of chlorides, sulfates, carbonates / bicarbonates), soil pH value, and organic matter content. A structured soil characteristic database was established to provide a data foundation for subsequent intelligent decision-making.

[0013] Step 2: Initial screening of curing agents based on feature matching to obtain candidate curing agents; Based on the soil characteristic database established in the first step, especially the core key characteristic parameters such as salt type and content, pH value and moisture content, and by comparing with the built-in "soil characteristics-curing agent performance" knowledge graph and matching rule library, a preliminary screening is carried out from a diversified curing agent material library including cement-based, polymer-based, industrial waste residue-based and composite types, to eliminate obviously incompatible curing agents and obtain a variety of candidate curing agents.

[0014] Step 3: Construct a multi-objective optimization model, perform multi-objective collaborative optimization decision-making, and obtain the optimal curing agent and its benchmark ratio; For candidate curing agents, a multi-objective optimization model is constructed with engineering performance (such as unconfined compressive strength and deformation modulus), economic cost (material and construction costs), resource consumption (curing agent dosage), and construction feasibility as the core. An advanced multi-objective optimization algorithm (such as NSGA-II) is used to solve the problem in parallel under given engineering constraints, generating a Pareto optimal solution set that represents the best balance between performance, cost, and dosage. By introducing decision-maker preferences or weight analysis, the optimal curing agent and its benchmark ratio are determined from the Pareto optimal solution set.

[0015] Step 4: Refined formulation design and durability verification; Centered on the baseline ratio of the optimal curing agent, a refined curing agent dosage gradient experiment was designed. Through indoor tests, the relationship curves of key performance indicators (such as strength and permeability coefficient) as a function of dosage were plotted to accurately determine the optimal dosage suitable for the current soil. Simultaneously, accelerated durability tests simulating harsh environmental conditions such as freeze-thaw cycles, wet-dry cycles, and chemical erosion were conducted to prospectively evaluate the long-term performance stability of the cured soil under the optimal ratio. The verification results were used as feedback information to revise and enrich the knowledge base of the optimization model.

[0016] Step 5: Through dynamic sensing of the construction process, the dosage of curing agent and construction process parameters are dynamically adjusted online; The aforementioned offline decision-making model is combined with a real-time sensing system at the construction site. Portable rapid detection equipment and IoT technology are used to acquire key characteristic parameters of the soil under construction (such as spatial variability of salinity and moisture content) in real time. Based on real-time construction data, a multi-objective optimization model is dynamically invoked or fine-tuned to achieve online dynamic adjustment of curing agent dosage and construction process parameters (such as mixing depth, uniformity, and curing conditions). This ensures that construction quality adapts to changes in the geological strata, achieving precise construction integrating "measurement-diagnosis-treatment".

[0017] Example This embodiment uses the treatment of high-salinity soft soil foundation in a proposed road construction project as an example to describe the method of the present invention in detail, including the following steps: Step 1: Extract key characteristic parameters of the soil through on-site investigation and sampling; On-site investigation and sampling: Drill core samples at grid points within the project area to obtain undisturbed and disturbed soil samples at different depths (0-2m, 2-4m), and record the groundwater level; conduct physicochemical tests on the soil samples indoors.

[0018] Salt content analysis: Determination of Cl in pore water using ion chromatography - SO4 2- Na + K + Mg 2+ Ca2+ Based on plasma concentration and combined with the total amount of easily soluble salts, it was determined that the main salts were NaCl and a small amount of Na2SO4, of which Cl... - The content, converted to NaCl, is approximately 3.2% of the dry weight of the soil sample, and SO4 content is... 2- The amount is approximately 0.5%.

[0019] Physical properties: The measured natural moisture content was 38%, liquid limit was 45%, plastic limit was 22%, and particle size analysis showed that it was silty clay.

[0020] Chemical indicators: The measured pH value was 8.5 and the organic matter content was 1.2%.

[0021] Database establishment: Enter all the above data, along with the sampling location coordinates and depth information, into the soil feature database.

[0022] Step 2: Initial screening of curing agents based on feature matching to obtain candidate curing agents; Calling the built-in knowledge base, based on core key feature parameters (Cl) - Content >3.0%, SO4 2- The following conditions were considered for matching: pH > 8, moisture content > 35%; matching rules: high chloride salt environment has potential harm to the long-term durability of ordinary Portland cement, and sulfate poses an expansion risk; high moisture content requires consideration of material dispersibility and hydration control. Therefore, "sulfate-resistant cement-based composite material", "slag-alkali activated geopolymer" and "cement-polymer composite system" were selected as candidate curing agents, and the option of using ordinary Portland cement alone was excluded.

[0023] Step 3: Construct a multi-objective optimization model, and obtain the optimal curing agent and its benchmark ratio through multi-objective collaborative optimization decision-making; The three optimization objectives are 28-day unconfined compressive strength (target: ≥X MPa), cost of materials per cubic meter of reinforced soil (target: ≤Y yuan), and total cementitious material content (target: ≤Z %). Constraints (such as workability requirements and initial setting time) are set to construct a multi-objective optimization model. The NSGA-II algorithm is used to iteratively solve the multi-objective optimization model to obtain the Pareto optimal solution set, such as... Figure 2 As shown; from the Pareto optimal solution set, combined with the project's preference for high early strength requirements, a balance point scheme is selected: the optimal curing agent is a composite system of "PO 42.5 cement (with appropriate amount of gypsum for setting) - fly ash - non-ionic PAM (molecular weight 15 million)", and its reference mass ratio is recommended as follows: cement: fly ash: PAM = 7% : 3% : 0.4% (percentage of dry soil mass).

[0024] Step 4: Refined formulation design and durability verification; Gradient test: Taking the above-mentioned benchmark mix ratio (total admixture 10.4%) as the center, while keeping the ratio of cement to fly ash constant, the total cementitious material admixture was set to five gradients of 9%, 10%, 10.4%, 11%, and 12%, and the PAM admixture was adjusted proportionally; samples were prepared and their 7-day and 28-day unconfined compressive strengths were tested.

[0025] Determine the optimal dosage: Plot a strength-curing agent dosage curve, such as... Figure 3 As shown, when the total admixture content increases from 10.4% to 11%, the strength growth rate slows down significantly and enters a plateau period. Therefore, from an economic perspective, the optimal admixture content is determined to be 11% (of which cement is 7.7%, fly ash is 3.3%, and PAM is 0.44%).

[0026] Durability verification: Samples were prepared according to the optimal dosage and subjected to wet-dry cycle (immersion-drying) tests and sodium sulfate solution immersion corrosion tests. After 5 wet-dry cycles, the strength retention rate was 92%; after immersion in 5% Na2SO4 solution for 60 days, the sample remained intact, without expansion or cracking, and showed no strength reduction. The results meet the design requirements.

[0027] Step 5: Through dynamic sensing of the construction process, the dosage of curing agent and construction process parameters are dynamically adjusted online; Key characteristic parameters of the construction soil can be acquired in real time, and a multi-objective optimization model can be dynamically called or fine-tuned based on real-time construction data to achieve online dynamic adjustment of curing agent dosage and construction process parameters.

[0028] Any aspects not covered in this invention are applicable to existing technologies.

Claims

1. A method for selecting and constructing a solidifying agent for high-salt soft soil, characterized in that, Includes the following steps: Step 1: Extract key soil characteristic parameters through on-site investigation and sampling, and construct a soil characteristic database; Step 2: Initial screening of curing agents based on feature matching to obtain candidate curing agents; Step 3: Construct a multi-objective optimization model with engineering performance, economic cost, resource consumption and construction feasibility as the core, solve the multi-objective optimization model to obtain the optimal curing agent and its benchmark ratio; Step 4: Based on the baseline ratio of the optimal curing agent, refine the mixing ratio; Centered on the baseline ratio of the optimal curing agent, a refined dosage gradient experiment was designed, and the relationship curves of key performance indicators with dosage were plotted to obtain the optimal dosage. Step 5: Dynamically monitor the construction process and dynamically adjust the dosage of curing agent and construction process parameters online; The system acquires key characteristic parameters of the construction soil in real time, dynamically calls or fine-tunes the multi-objective optimization model based on real-time construction data, and solves the multi-objective optimization model to achieve online dynamic adjustment of curing agent dosage and construction process parameters.

2. The method for selecting and constructing a high-salt soft soil stabilizing agent according to claim 1, characterized in that, The key characteristic parameters of the soil include physical indicators, chemical salt content parameters, pH value, and organic matter content.

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