@datadrivenconstruction
DataDrivenConstruction.io - empowering digital transformation in the construction industry. We help teams automate data workflows, leverage open data standards
238 skills1 repository 359
Predict project duration using k-NN and regression. Estimate timeline based on similar historical projects.
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Extract and analyze data from construction ERP systems. Pull project data for analytics, reporting, and integration.
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Build validation rules for construction data. Create RegEx and logic-based validation for BIM elements, cost codes, and schedule data.
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What-if analysis for construction projects: model different scenarios and their cost/schedule/resource impacts. Compare alternatives and optimize decisions.
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Analyze construction drawings to extract dimensions, annotations, symbols, and metadata. Support quantity takeoff and design review automation.
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Automate construction data workflows. Build ETL pipelines and DAG workflows for recurring tasks.
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Extract structured data from construction specifications. Parse CSI sections, requirements, submittals, and product data from spec documents.
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Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.
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Extract structured data from construction PDFs. Convert specifications, BOMs, schedules, and reports from PDF to Excel/CSV/JSON. Use OCR for scanned documents and pdfplumber for native PDFs.
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Convert construction data to/from Parquet format. Optimize storage, enable fast queries, and integrate with data lakehouses.
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Track data origin, transformations, and flow through construction systems. Essential for audit trails, compliance, and debugging data issues.
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Automated pipeline for retraining ML models with new construction data. Monitor model drift, trigger retraining, and validate model performance.
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Build ML models for construction predictions. Train and evaluate custom models for cost, duration, and risk prediction.
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Build construction project estimates. Generate detailed cost breakdowns with labor, materials, equipment, and overhead.
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Calculate construction labor rates with overhead, benefits, and productivity factors. Regional rate databases and crew composition.
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Automatically generate estimates from QTO data. Apply pricing rules to BIM quantities for cost estimates.
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Profile construction data to understand characteristics, distributions, quality metrics, and patterns. Essential for data quality assessment and ETL planning.
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Classify construction data by type (structured, unstructured, semi-structured). Analyze data sources and recommend appropriate storage/processing methods
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Detect and map data silos in construction organizations. Identify disconnected data sources and integration opportunities
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Read and parse XML from construction systems - P6 schedules, BSDD exports, IFC-XML, COBie-XML. Convert to pandas DataFrames.
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Extract, clean, and organize legacy construction data from archives. Migrate historical project data, cost records, and schedules into modern formats.
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Semantic search in the DDC CWICR construction cost database using vector embeddings (BGE-M3, 1024-dim, per-language Qdrant collections). Find similar work items and resources for cost estimation across 8 national bases and 30 markets in 26 languages.
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Break down CWICR work items into component resources. Decompose aggregate items, analyze resource composition, and generate detailed bills of resources.
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Integrate open construction datasets. Combine open data sources for enhanced analysis
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