LLM Data Automation for Construction

Overview

Based on DDC methodology (Chapter 2.3), this skill enables automation of construction data processing using Large Language Models (LLM). Instead of manually coding data transformations, you describe what you need in natural language, and the LLM generates the necessary Python/Pandas code.

Book Reference: "Pandas DataFrame и LLM ChatGPT" / "Pandas DataFrame and LLM ChatGPT"

"LLM-модели, такие как ChatGPT и LLaMA, позволяют специалистам без глубоких знаний программирования внести свой вклад в автоматизацию и улучшение бизнес-процессов компании." — DDC Book, Chapter 2.3

Quick Start

Option 1: Use ChatGPT/Claude Online

Simply describe your data processing task in natural language:

Prompt: "Write Python code to read an Excel file with construction materials, filter rows where quantity > 100, and save to CSV."

Option 2: Run Local LLM (Ollama)

Install Ollama from ollama.com

ollama pull mistral

Run a query

ollama run mistral "Write Pandas code to calculate total cost from quantity * unit_price"

Option 3: Use LM Studio (GUI)

• Download from lmstudio.ai

• Install and select a model (e.g., Mistral, LLaMA)

• Start chatting with your local AI

Core Concepts

DataFrame as Universal Format

import pandas as pd

Construction project as DataFrame

Rows = elements, Columns = attributes

df = pd.DataFrame({ 'element_id': ['W001', 'W002', 'C001'], 'category': ['Wall', 'Wall', 'Column'], 'material': ['Concrete', 'Brick', 'Steel'], 'volume_m3': [45.5, 32.0, 8.2], 'cost_per_m3': [150, 80, 450] })

Calculate total cost

df['total_cost'] = df['volume_m3'] * df['cost_per_m3'] print(df)

LLM Prompts for Construction Tasks

Data Import:

"Write code to import Excel file with construction schedule, parse dates, and create a Pandas DataFrame"

Data Filtering:

"Filter construction elements where category is 'Structural' and cost exceeds budget limit of 50000"

Data Aggregation:

"Group construction data by floor level, calculate total volume and cost for each floor"

Report Generation:

"Create summary report with material quantities grouped by category, export to Excel with formatting"

Common Use Cases

1. Extract Data from PDF Documents

Prompt to ChatGPT:

"Write code to extract tables from PDF and convert to DataFrame"

import pdfplumber import pandas as pd

def pdf_to_dataframe(pdf_path): """Extract tables from PDF file""" all_tables = [] with pdfplumber.open(pdf_path) as pdf: for page in pdf.pages: tables = page.extract_tables() for table in tables: if table: df = pd.DataFrame(table[1:], columns=table[0]) all_tables.append(df)

if all_tables:
    return pd.concat(all_tables, ignore_index=True)
return pd.DataFrame()

Usage

df = pdf_to_dataframe("construction_spec.pdf") df.to_excel("extracted_data.xlsx", index=False)

2. Process BIM Element Data

Prompt: "Analyze BIM elements, group by category, calculate volumes"

import pandas as pd

def analyze_bim_elements(csv_path): """Analyze BIM element data from CSV export""" df = pd.read_csv(csv_path)

# Group by category
summary = df.groupby('Category').agg({
    'Volume': 'sum',
    'Area': 'sum',
    'ElementId': 'count'
}).rename(columns={'ElementId': 'Count'})

return summary

Usage

summary = analyze_bim_elements("revit_export.csv") print(summary)

3. Cost Estimation Pipeline

Prompt: "Create cost estimation from quantities and unit prices"

import pandas as pd

def calculate_cost_estimate(quantities_df, prices_df): """ Calculate project cost estimate

Args:
    quantities_df: DataFrame with columns [item_code, quantity]
    prices_df: DataFrame with columns [item_code, unit_price, unit]

Returns:
    DataFrame with cost calculations
"""
# Merge quantities with prices
result = quantities_df.merge(prices_df, on='item_code', how='left')

# Calculate costs
result['total_cost'] = result['quantity'] * result['unit_price']

# Add summary
result['cost_percentage'] = (result['total_cost'] /
                              result['total_cost'].sum() * 100).round(2)

return result

Usage

quantities = pd.DataFrame({ 'item_code': ['C001', 'S001', 'W001'], 'quantity': [150, 2000, 500] })

prices = pd.DataFrame({ 'item_code': ['C001', 'S001', 'W001'], 'unit_price': [120, 45, 85], 'unit': ['m3', 'kg', 'm2'] })

estimate = calculate_cost_estimate(quantities, prices) print(estimate)

4. Schedule Data Processing

Prompt: "Parse construction schedule, calculate durations, identify delays"

import pandas as pd from datetime import datetime

def analyze_schedule(schedule_path): """Analyze construction schedule for delays""" df = pd.read_excel(schedule_path)

# Parse dates
df['start_date'] = pd.to_datetime(df['start_date'])
df['end_date'] = pd.to_datetime(df['end_date'])
df['actual_end'] = pd.to_datetime(df['actual_end'])

# Calculate durations
df['planned_duration'] = (df['end_date'] - df['start_date']).dt.days
df['actual_duration'] = (df['actual_end'] - df['start_date']).dt.days

# Identify delays
df['delay_days'] = df['actual_duration'] - df['planned_duration']
df['is_delayed'] = df['delay_days'] > 0

return df

Usage

schedule = analyze_schedule("project_schedule.xlsx") delayed_tasks = schedule[schedule['is_delayed']] print(f"Delayed tasks: {len(delayed_tasks)}")

Local LLM Setup (No Internet Required)

Using Ollama

Install

curl -fsSL https://ollama.com/install.sh | sh

Download models

ollama pull mistral # General purpose, 7B params ollama pull codellama # Code-focused ollama pull deepseek-coder # Best for coding tasks

Run

ollama run mistral "Write Pandas code to merge two DataFrames on project_id"

Using LlamaIndex for Company Documents

Load company documents into local LLM

from llama_index import SimpleDirectoryReader, VectorStoreIndex

Read all PDFs from folder

reader = SimpleDirectoryReader("company_documents/") documents = reader.load_data()

Create searchable index

index = VectorStoreIndex.from_documents(documents)

Query your documents

query_engine = index.as_query_engine() response = query_engine.query( "What are the standard concrete mix specifications?" ) print(response)

IDE Recommendations

IDE Best For Features

Jupyter Notebook Learning, experiments Interactive cells, visualizations

Google Colab Free GPU, quick start Cloud-based, pre-installed libs

VS Code Professional development Extensions, GitHub Copilot

PyCharm Large projects Advanced debugging, refactoring

Quick Setup with Jupyter

pip install jupyter pandas openpyxl pdfplumber jupyter notebook

Best Practices

• Start Simple: Begin with clear, specific prompts

• Iterate: Refine prompts based on results

• Validate: Always check generated code before running

• Document: Save working prompts for reuse

• Secure: Use local LLM for sensitive company data

Common Prompts Library

Data Import

• "Read Excel file and show first 10 rows"

• "Import CSV with custom delimiter and encoding"

• "Load multiple Excel sheets into dictionary of DataFrames"

Data Cleaning

• "Remove duplicate rows based on element_id"

• "Fill missing values with column mean"

• "Convert column to numeric, handling errors"

Data Analysis

• "Calculate descriptive statistics for numeric columns"

• "Find correlation between cost and duration"

• "Identify outliers using IQR method"

Data Export

• "Export to Excel with multiple sheets"

• "Save to CSV with specific encoding"

• "Generate formatted PDF report"

Resources

• Book: "Data-Driven Construction" by Artem Boiko, Chapter 2.3

• Website: https://datadrivenconstruction.io

• Pandas Documentation: https://pandas.pydata.org/docs/

• Ollama: https://ollama.com

• LM Studio: https://lmstudio.ai

• Google Colab: https://colab.research.google.com

Next Steps

• See pandas-construction-analysis for advanced Pandas operations

• See pdf-to-structured for document processing

• See etl-pipeline for automated data pipelines

• See rag-construction for RAG implementation with construction documents