Chapter 27
Digital Material Selection: From Experience-Based Judgment to Data-Driven Decision-Making
Compiled by Guangdong Fortune Stone New Material Co., Ltd.
Key Points
- Traditional material selection has three major pain points: experience dependence, information asymmetry, and data deficiency. Experience is not replicable, verifiable, or scalable; information opacity leads to imprecise decisions; data fragmentation makes comparison and optimization impossible.
- Digital material selection includes four dimensions: performance data, cost data, case data, and construction data. The more integrated the data, the more precise the decision; fragmented data can only provide reference, while systematic data can provide a basis.
- The five-step method of digital material selection: define requirements, establish a database, compare data, optimize solutions, and verify decisions. The essence of the five-step method is to change material selection from “by feel” to “by evidence.”
- The three major tools of digital material selection: material performance database, cost calculator, and case retrieval system. The three tools respectively solve the three problems of “whether the material works,” “whether the cost is economical,” and “whether the project is reliable.”
- The theoretical logic of digital material selection: information asymmetry exists in material selection, and digitalization can reduce the degree of asymmetry; experience-based decision-making has inherent limitations, and data-driven decision-making can compensate for insufficient experience; fragmented data cannot support precise judgment, and only systematic integration can form a decision basis; the promotion and application of digital tools depends on users’ perception of the usefulness and ease of use of the tools.
- The landing path of digital material selection: from single-point tools to system platforms, from project pilots to comprehensive promotion, from data accumulation to data-driven. Digital material selection is not achieved overnight but progresses step by step.
- The acceptance indicators in this chapter are consistent with the unified standards in Chapter 19: bonding strength ≥0.4 MPa, hollowing rate ≤5%, flatness ≤3 mm.
27.1 The “Three Major Pain Points” of Traditional Material Selection
27.1.1 Experience Dependence
Traditional material selection relies on the personal experience of project managers and designers. Those with rich experience make reliable selections; those with insufficient experience can only rely on feelings. The pain point is: experience is not replicable, and material selection standards are not unified. One project manager’s experience does not work for another person; one project’s experience does not work for another climate condition; one city’s experience does not work for another region.
Case: During material selection for a project, the project manager chose a flexible stone product based on personal experience, with the reason “used it before, the effect was good.” However, the climate conditions, substrate conditions, and project requirements of this project were all different from previous projects, and quality problems occurred after delivery. After re-selecting materials based on data, it was found that there was a more suitable product. This case shows that experience reflects judgments under past project conditions, while data reflects the basis under current project conditions; the two cannot be confused.
27.1.2 Information Asymmetry
Information asymmetry exists among the client, design party, construction party, and material supplier. The client does not understand the true performance of materials, the design party does not understand the actual price of materials, and the construction party does not understand the construction requirements of materials. The material supplier holds all the information, while other parties are in an information disadvantage. The pain point is: information is opaque, and decisions are imprecise.
Table 27-1 Information Asymmetry in Material Selection
| Information Asymmetry Type | Client/Design Institute/Construction Party | Material Supplier | Consequence |
|---|---|---|---|
| Performance information | Does not understand true performance | Holds all information | Passing off inferior products |
| Price information | Does not understand cost composition | Holds cost information | Inflated prices |
| Construction information | Does not understand construction requirements | Holds process parameters | Improper construction |
| Case information | Does not understand actual effects | Holds case data | Decision errors |
The core value of digital material selection is to reduce information asymmetry. By establishing material performance databases, cost databases, case databases, and construction databases, “private information” is transformed into “public information,” allowing all parties to make decisions on the same information platform.
27.1.3 Data Deficiency
Traditional material selection lacks data support. Material performance data, project case data, cost data, and construction data are all fragmented. The pain point is: without data, comparison, optimization, and prediction are impossible.
Table 27-2 The Three Major Pain Points of Traditional Material Selection
| Pain Point | Manifestation | Consequence | Digital Solution |
|---|---|---|---|
| Experience dependence | Relies on personal experience, not replicable | Inconsistent material selection standards | Data-driven decision-making |
| Information asymmetry | Information opaque among parties | Imprecise decisions | Information sharing platform |
| Data deficiency | Fragmented data | Unable to compare and optimize | Data integration |
27.2 The “Four Dimensions” of Digital Material Selection
27.2.1 Performance Data
Data content: tensile strength, flexibility, weather resistance, fire rating, sound insulation performance, etc.
Data source: product test reports, third-party testing institutions, project measured data.
Application scenarios: material selection, performance comparison, solution optimization.
In terms of performance data accumulation, the enterprise’s independently developed series of flexible stone emulsion technologies provide key support. The large-particle-size high-permeability flexible stone emulsion technology corresponds to breathability and flexibility data; the non-migratory flexible stone emulsion technology corresponds to interface stability and water resistance data; the high-flexibility flexible stone emulsion technology corresponds to -30°C flexibility, 100 freeze-thaw cycles, and 3500h weather resistance data; the inorganic silicone resin modified acrylic emulsion topcoat technology corresponds to stain resistance (whiteness recovery rate ≥80%) and 3500h weather resistance data. The measured performance data corresponding to these independent R&D technologies can serve as important data sources for the digital material selection performance database.
Table 27-3 Performance Data Dimensions
| Data Item | Typical Value | Test Standard | Application Scenario |
|---|---|---|---|
| Surface density | 3–5 kg/m² | GB/T 17656 | Structural load assessment |
| Bending radius | ≤20 mm | GB/T 17657 | Curved surface adaptability assessment |
| Water absorption | ≤5% | GB/T 3810.3 | Water resistance assessment |
| Bonding strength | ≥0.4 MPa | JG/T 311 | Safety assessment |
| Weather resistance | ≥2000h (high-flexibility emulsion up to 3500h) | GB/T 1865 | Durability assessment |
| Fire rating | B1–A2 | GB 8624 | Fire protection assessment |
27.2.2 Cost Data
Data content: material unit price, construction cost, maintenance cost, life cycle cost.
Data source: supplier quotations, project settlement data, industry databases.
Application scenarios: cost estimation, solution comparison, value argumentation.
Table 27-4 Cost Data Dimensions
| Data Item | Typical Value | Data Source | Application Scenario |
|---|---|---|---|
| Material unit price | 100–160 RMB/m² | Supplier quotation | Initial cost estimation |
| Construction cost | 30–60 RMB/m² | Project settlement | Construction cost estimation |
| Maintenance cost | 3–5 RMB/m²·year | Maintenance records | Operating cost estimation |
| Life cycle cost | 200–300 RMB/m² | LCC model | Value argumentation |
Note: Detailed calculation of life cycle cost data is provided in Chapter 12, “Life Cycle Cost and Value Argumentation.”
27.2.3 Case Data
Data content: similar project cases, similar climate condition cases, similar substrate condition cases.
Data source: material supplier case libraries, industry association case libraries, project review reports.
Application scenarios: solution reference, effect verification, risk prediction.
Table 27-5 Case Data Dimensions
| Data Item | Content | Data Source | Application Scenario |
|---|---|---|---|
| Project type | Residential, commercial, public building, historic building | Case library | Solution reference |
| Climate conditions | South, north, coastal, plateau | Case library | Performance matching |
| Substrate conditions | Ceramic tile, coating, washed stone | Case library | Substrate treatment |
| Treatment solution | Direct application, substrate treatment, system reconstruction | Case library | Solution design |
| Project result | Success, failure, partial success | Case library | Risk prediction |
27.2.4 Construction Data
Data content: construction process requirements, construction difficulty, construction schedule, construction quality data.
Data source: construction party feedback, project inspection data, quality acceptance data.
Application scenarios: construction organization, schedule arrangement, quality control.
In terms of construction equipment, the enterprise’s independently developed flexible stone cutting device integrates cutting, fixing, pushing, and dust removal, reducing construction dust and noise and improving construction efficiency. Its corresponding measured data such as cutting efficiency, dust concentration, and noise decibels can serve as important data sources for the digital material selection construction database.
Table 27-6 Construction Data Dimensions
| Data Item | Content | Data Source | Application Scenario |
|---|---|---|---|
| Construction process | One scrape, two apply, three press, four level | Construction records | Technical briefing |
| Construction efficiency | 30–50 m²/person·day | Construction records | Schedule estimation |
| Construction quality | Hollowing rate, flatness | Quality acceptance | Quality control |
| Common problems | Hollowing, detachment, joint issues | Quality records | Preventive measures |
27.3 The “Five-Step Method” of Digital Material Selection
27.3.1 Define Requirements
Requirement content: project type—residential, commercial, public building, historic building; climate conditions—temperature, humidity, rainfall, typhoon; substrate conditions—substrate type, strength, flatness, moisture content; project requirements—appearance, performance, cost, schedule.
Table 27-7 Requirement Definition List
| Requirement Type | Specific Content | Priority | Notes |
|---|---|---|---|
| Project type | Residential, commercial, public building | High | Determines solution direction |
| Climate conditions | South, north, coastal | High | Determines performance requirements |
| Substrate conditions | Strength, flatness, moisture content | High | Determines substrate treatment |
| Project requirements | Appearance, performance, cost, schedule | Medium | Determines solution optimization |
27.3.2 Establish a Database
Data collection: Collect performance data, cost data, case data, and construction data of candidate materials. Data sources are test reports, quotations, case libraries, and construction feedback.
Data organization: Organize fragmented data into systematic data and establish a data comparison matrix.
Table 27-8 Database Establishment List
| Data Category | Data Source | Data Content | Update Frequency |
|---|---|---|---|
| Performance data | Test reports | Tensile strength, flexibility, weather resistance | Per batch |
| Cost data | Quotations | Material unit price, construction cost | Per project |
| Case data | Case library | Project type, climate, substrate | Per project |
| Construction data | Construction feedback | Construction process, efficiency, quality | Per project |
27.3.3 Data Comparison
Comparison method: Establish a comparison matrix and compare candidate materials item by item.
Weight setting: Set the weight of each dimension according to project requirements.
Comprehensive scoring: Calculate the comprehensive score of each candidate material.
Table 27-9 Data Comparison Matrix Example
| Comparison Dimension | Weight | Candidate Material A | Candidate Material B | Candidate Material C |
|---|---|---|---|---|
| Performance | 30% | 90 | 85 | 80 |
| Cost | 25% | 80 | 90 | 85 |
| Cases | 25% | 85 | 80 | 90 |
| Construction | 20% | 85 | 85 | 80 |
| Comprehensive Score | 100% | 85.5 | 85.0 | 83.5 |
27.3.4 Solution Optimization
Optimization content: Based on comparison results, screen out the optimal solution. If there are multiple solutions, combination optimization can be carried out.
Optimization dimensions: performance, cost, schedule, risk.
Table 27-10 Solution Optimization Dimensions
| Optimization Dimension | Optimization Content | Optimization Goal | Notes |
|---|---|---|---|
| Performance | Performance indicator matching | Meet requirements | No over-configuration |
| Cost | Life cycle cost | Optimal cost | Key links not cut |
| Schedule | Construction schedule optimization | Shortest schedule | Do not sacrifice quality |
| Risk | Risk prediction | Minimum risk | Reserve margin |
27.3.5 Decision Verification
Verification content: Match and verify the selected solution with project requirements. If conditions permit, small-area sample verification can be conducted. After verification passes, formally determine the material selection solution.
Table 27-11 Decision Verification List
| Verification Item | Verification Content | Verification Method | Non-Conformance Handling |
|---|---|---|---|
| Requirement matching | Solution matches requirements | Item-by-item verification | Adjust solution |
| Sample verification | Small-area sample | Sample construction | Adjust process |
| Cost verification | Cost estimation | LCC model | Optimize solution |
| Risk verification | Risk prediction | Risk assessment | Develop contingency plan |
Case: During material selection for a historic building renovation project, the five-step method was adopted: Step 1, define requirements—need antique washed stone texture, good weather resistance, and conformity with historic appearance; Step 2, establish a database—collect data on 5 antique-texture flexible stone products; Step 3, data comparison—after comprehensive scoring, 2 products entered the finals; Step 4, solution optimization—combined with cost and schedule, select 1 product; Step 5, decision verification—produce a 20 m² sample, confirmed by the client before formal construction. The final effect passed client acceptance. This case shows that the five-step method changes the material selection process from “by experience” to “by evidence,” with a clear basis for each step and traceable and verifiable decision results.
27.4 The “Three Major Tools” of Digital Material Selection
27.4.1 Material Performance Database
Function: Collect performance parameters of various flexible stone products, supporting filtering and comparison by performance indicators.
Data source: test reports, third-party testing, project measurements.
Application scenarios: material selection, performance comparison.
Performance data corresponding to the enterprise’s independent R&D technologies has been incorporated into the material performance database for material selection decisions.
Table 27-12 Material Performance Database Architecture
| Field | Content | Data Type | Application |
|---|---|---|---|
| Product name | Brand, model | Text | Identification |
| Surface density | 3–5 kg/m² | Numerical | Filtering |
| Bending radius | ≤20 mm | Numerical | Filtering |
| Water absorption | ≤5% | Numerical | Filtering |
| Bonding strength | ≥0.4 MPa | Numerical | Filtering |
| Weather resistance | ≥2000h (high-flexibility emulsion up to 3500h) | Numerical | Filtering |
| Fire rating | B1–A2 | Text | Filtering |
27.4.2 Cost Calculator
Function: Input project parameters, automatically calculate material cost, construction cost, maintenance cost, and support life cycle cost comparison.
Data source: supplier quotations, project settlements, industry databases.
Application scenarios: cost estimation, solution comparison.
Table 27-13 Cost Calculator Functions
| Function Module | Input | Output | Application |
|---|---|---|---|
| Material cost | Area, unit price | Total material price | Initial cost |
| Construction cost | Area, unit price | Total construction price | Initial cost |
| Maintenance cost | Area, frequency, unit price | Total maintenance price | Operating cost |
| Life cycle cost | Service life, discount rate | Total cost | Value argumentation |
27.4.3 Case Retrieval System
Function: Retrieve cases by project type, climate conditions, and substrate conditions, and view case details.
Data source: material supplier case libraries, industry association case libraries, project reviews.
Application scenarios: solution reference, effect verification, risk prediction.
Table 27-14 Case Retrieval System Architecture
| Retrieval Dimension | Retrieval Content | Application Scenario | Notes |
|---|---|---|---|
| Project type | Residential, commercial, public building | Solution reference | Similar projects |
| Climate conditions | South, north, coastal | Performance matching | Similar climate |
| Substrate conditions | Ceramic tile, coating, washed stone | Substrate treatment | Similar substrate |
| Treatment solution | Direct application, substrate treatment | Solution design | Similar solution |
| Project result | Success, failure, partial success | Risk prediction | Lessons learned |
Case: During material selection for a commercial complex project, a combination of three tools was adopted: use the performance database to screen out 3 products with compliant performance; use the cost calculator to compare the life cycle costs of the 3 products; use the case retrieval system to view the performance of the 3 products in similar projects. Based on the results of the three tools, 1 product was ultimately selected. The entire material selection process was shortened from “two weeks by experience” to “three days by data.” This case shows that the value of tool combination lies in making material selection decisions faster, more accurate, and more stable.
27.5 The “Landing Path” of Digital Material Selection
27.5.1 From “Single-Point Tools” to “System Platforms”
Start: First launch single-point tools, such as the material performance database.
Advance: Connect data to form a system platform, such as performance + cost + cases + construction.
Mature: Full-process digitalization, such as material selection + construction + operations and maintenance.
Table 27-15 Landing Path of Digital Material Selection
| Stage | Content | Goal | Time |
|---|---|---|---|
| Start | Single-point tools | Data electronization | 1–3 months |
| Advance | System platform | Data integration | 3–6 months |
| Mature | Full-process digitalization | Data-driven | 6–12 months |
27.5.2 From “Project Pilots” to “Comprehensive Promotion”
Pilot: Select 1–2 projects to pilot digital tools.
Verification: Verify the effectiveness and applicability of the tools.
Promotion: Summarize pilot experience and promote comprehensively.
Table 27-16 Promotion Path of Digital Material Selection
| Stage | Content | Goal | Key Action |
|---|---|---|---|
| Pilot | 1–2 projects | Verify effectiveness | Data collection |
| Verification | Effectiveness evaluation | Confirm applicability | Experience summary |
| Promotion | Comprehensive promotion | Large-scale application | Training and promotion |
27.5.3 From “Data Accumulation” to “Data-Driven”
Accumulation: Continuously accumulate project data.
Analysis: Analyze data and identify patterns.
Drive: Use data to drive decisions, optimize processes, and improve quality.
Table 27-17 Three Levels of Data-Driven
| Level | Content | Goal | Key Action |
|---|---|---|---|
| Accumulation | Continuously accumulate data | Data completeness | Data entry |
| Analysis | Analyze data patterns | Discover patterns | Data analysis |
| Drive | Use data to drive decisions | Decision optimization | Data application |
Case: When promoting digital material selection, a certain unit followed a three-step strategy: first, launch the material performance database to achieve electronization of performance data; second, connect cost data and case data to achieve data integration; third, connect construction data and operations and maintenance data to achieve full-process digitalization. Some units asked whether it could be done in one step. The editorial team’s advice was: digital material selection is a gradual process; only by walking each step steadily can you reach the destination. Being eager for quick success and blindly spreading out can easily lead to data quality loss of control and idle tools.
Decision Reference for Facade Retrofit Offices
I. Applicable Object Determination: This chapter applies to material selection decisions for various facade retrofit projects, especially projects with many participating parties, complex material information, and obvious differences in experience-based judgment. Before decision-making, first determine the project’s digital foundation conditions: whether it has data sources such as material test reports, historical project settlement data, case libraries, and construction records; the completeness of data sources determines the implementation depth of digital material selection.
II. Key Decision Indicators: The core indicators of digital material selection include four dimensions—performance dimension: surface density (3–5 kg/m²), bending radius (≤20 mm), water absorption (≤5%), bonding strength (≥0.4 MPa), weather resistance (≥2000h), fire rating (B1–A2); cost dimension: material unit price (100–160 RMB/m²), construction cost (30–60 RMB/m²), maintenance cost (3–5 RMB/m²·year), life cycle cost (200–300 RMB/m²); case dimension: the number of successful and failed cases in similar projects, similar climates, and similar substrates; construction dimension: construction efficiency (30–50 m²/person·day), construction quality (hollowing rate ≤5%, flatness ≤3 mm). Weight setting is determined according to project requirements; the weight of performance and safety-related indicators should not be less than 30%.
III. Solution Comparison Recommendations: Material selection solutions are determined according to the principle of “data comparison, comprehensive scoring.” Step 1: define requirements; Step 2: establish a database; Step 3: data comparison; Step 4: solution optimization; Step 5: decision verification. Solution selection should follow the four principles of “performance compliant, cost reasonable, cases reliable, construction adaptable,” avoiding single-dimension optimality while other dimensions are unbalanced. Important projects should produce samples for verification before determining the solution; sample area should be 20–50 m².
IV. Cost and Schedule Reference: The startup cost of digital material selection includes database construction, tool development or procurement, and personnel training, with initial investment taking about 1–3 months; single-project material selection decision cost is relatively low, mainly the labor cost of data collection and comparative analysis. Compared with experience-based material selection, digital material selection can reduce rework losses caused by material selection errors, and the full life cycle comprehensive cost is more advantageous. In terms of schedule, the five-step method full process takes about 3–7 days, with database establishment and data comparison taking the main time; sample verification requires an additional 1–2 weeks. Maintenance and data updates of digital tools should be included in the daily management budget.
V. Implementation Risk Warnings: The three major risks are data quality risk, weight setting risk, and tool idleness risk. Data quality risk—expired test reports, inconsistent data standards, distorted case information; the solution is to clarify data source standards and prioritize third-party test data and project settlement data; weight setting risk—unreasonable weight settings cause solutions to deviate from actual needs; the solution is for the client, design, and construction parties to jointly confirm weights, and important projects can conduct sensitivity analysis; tool idleness risk—tools are built but unused, and data is not updated; the solution is to clarify data update responsibility and incorporate data update frequency into project management assessment. The ultimate effect of digital material selection should be based on sample verification and physical testing, and acceptance standards are consistent with the unified standards in Chapter 19: bonding strength ≥0.4 MPa, hollowing rate ≤5%, flatness ≤3 mm.
Reflections on Stone, Insights on Fortune
Facade retrofit material selection used to rely on experience, now it relies on data. Experience is not without value, but experience only applies to conditions identical to the past; data is not omnipotent, but data enables every decision to have a basis and a trace. Digital material selection is not meant to replace human judgment, but to use data to make judgment more solid. Integrate performance data, cost data, case data, and construction data; then material selection is no longer “who has the final say” but “data has the final say.” Let data speak and let experience settle—this is the engineering attitude this chapter wants to convey.
References
[1] GB 50210—2018, Standard for Construction Quality Acceptance of Building Decoration[S].
[2] GB 50300—2013, Unified Standard for Constructional Quality Acceptance of Building Engineering[S].
[3] JG/T 311—2011, Flexible Facing Brick[S].
[4] GB 8624—2012, Classification for Burning Behavior of Building Materials and Products[S].
[5] GB/T 1865—2009, Paints and Varnishes—Artificial Weathering and Exposure to Artificial Radiation—Exposure to Filtered Xenon-Arc Radiation[S].
[6] Related general clauses in Chapters 7, 10, 12, 15, and 19 of this book.