DOI: 10.5281/zenodo.17668012
title: AI-TCN-RiskMap: An AI-Driven Construction Management Framework

description: AI-TCN-RiskMap: An AI-Driven Construction Management Framework

A Temporal Convolution and Risk Map–Integrated Model for Dynamic Construction Management

Overview

AI-TCN-RiskMap is a unified construction management framework that integrates Temporal Convolutional Networks (TCN) with a Temporal Risk Mapping Strategy (TRMS) to support time-dependent risk prediction, dynamic decision-making, and spatial–temporal safety analysis in construction projects.

This framework is based on the study “An AI construction management model integrating temporal convolution and risk map construction strategies.” It provides a complete artificial-intelligence-driven pipeline for processing construction time-series data, modeling spatial site conditions, generating risk maps, and enabling intelligent strategies for schedule, cost, and safety management.

The model offers interpretability, efficiency, and high adaptability across different construction scenarios, enabling more proactive and data-driven construction planning.



Features

● Temporal Convolutional Risk Management Model (TCRMM)

TCRMM uses multi-layer temporal convolutions to extract dynamic dependencies in construction time-series:



Resource usage

Schedule progress

Equipment operation signals

Delay indicators


It implements the temporal mapping function described in Eq. (1) and the enhanced multi-layer structure in Fig. 2, enabling robust learning of long-range project dependencies.

● Hybrid Adaptive Attention Mechanism (HAAM)

HAAM integrates:



Multi-head temporal self-attention

Channel-wise adaptive gating

Normalization and residual connections


This mechanism enhances temporal feature representation, improving prediction accuracy for delay risk and productivity fluctuations (Section 3.3).

● Risk Map Construction Module

The model builds a time-aware and region-aware spatial risk map, using:



A risk evolution formulation (Eq. 9–12)

Per-region risk estimators

Spatio-temporal fusion of features

Iterative refinement


This allows early detection of spatial hotspots, unsafe zones, or areas with abnormal operational patterns.

● Temporal Risk Mapping Strategy (TRMS)

TRMS is a high-level strategy combining:



Multimodal encoding of temporal + spatial inputs

Region-wise risk regression

Graph Propagation Layer for cross-region influence modeling

Multi-step iterative refinement


The method aligns with the temporal and spatial update framework illustrated in Fig. 6.

● Decision Adjustment Mechanism

Using a learnable adjustment function γ(pt, rt) (Eq. 5), the model dynamically tunes decisions based on predicted risk levels, enabling more realistic schedule/cost predictions under uncertain conditions.



Model Architecture

The complete model integrates two subsystems:

1. TCRMM (Temporal Convolutional Risk Management Model)

Processes global temporal sequences:



Multi-layer temporal convolutions

Hybrid adaptive attention

Temporal risk estimation

Risk-adjusted predictions


2. TRMS (Temporal Risk Mapping Strategy)

Processes per-region features:



Temporal and spatial feature encoders

Region-wise risk regression

Graph propagation for cross-region refinement

Final construction-risk map


The integrated pipeline outputs both temporal predictions (e.g., productivity, costs) and spatial risk distributions (risk maps).



Datasets

The framework was evaluated on four datasets—representing industrial-grade construction data—with both temporal and spatial modalities:

● Construction Project Temporal Data (CPTD)

Time-series of construction progress, productivity, equipment operation, materials flow.

● Risk Assessment Maps (RAM)

Spatial distributions of operational risks collected from real construction sites.

● Temporal Convolution Analysis in Construction (TCAC)

Sequential operational metrics used for TCN modeling and temporal feature extraction.

● AI-Based Construction Strategy Models (AICSM)

Strategy-level datasets including multi-stage decisions, resource plans, and delay annotations.

These datasets enable evaluating both temporal prediction and spatial risk estimation, supporting the integrated framework proposed in the study.



Evaluation

The experimental evaluation covers:

● Temporal Prediction Metrics



MAE

RMSE

R² Score

MAPE


● Risk Map Metrics



Risk MSE

High-Risk Mitigation Rate (HRMR)

Cross-region correlation consistency


● Model Efficiency



Parameters, FLOPs, runtime

Inference latency across CPU/GPU devices


● Baseline Comparisons

Compared against:



Standard TCN

LSTM and GRU

Transformer-based sequence models

Classical safety-risk assessment models


Key Findings



TCRMM significantly improves temporal accuracy under long-dependency scenarios.

TRMS improves spatial risk prediction, especially in cross-region correlated areas.

Integrated model outperforms all baselines in both temporal prediction and spatial risk mapping.

High efficiency and stable performance across all four datasets.




Applications

AI-TCN-RiskMap can be directly applied to:



Construction schedule prediction

Cost deviation estimation

Safety-risk hotspot detection

Resource allocation optimization

Multi-region spatio-temporal decision-support

Early-warning systems for construction sites

Intelligent construction strategy planning


The framework is suitable for smart-construction platforms, BIM-integrated systems, and digital-twin-based monitoring.



Future Work

According to the study’s outlook:



Real-time deployment with lightweight temporal models

Extension to multimodal inputs such as images, sensor data, and IoT streams

Incorporation of reinforcement learning for adaptive strategy selection

Large-scale construction knowledge-graph integration

Cross-project transfer learning

On-site robotic construction coordination


These directions aim to enhance adaptability, scalability, and decision-making intelligence.



License

This project will adopt the MIT License.



Acknowledgments

This work is built upon advancements in temporal modeling, spatial risk analysis, AI-driven construction monitoring, and digital-twin research. Special thanks to the contributors and organizations supporting intelligent construction technologies.