Smart Technologies in Construction and Transportation Infrastructure: A Review of AI, IoT, and Data-Driven Innovations

While the field of civil engineering has traditionally been dominated by hands-on and experience-based techniques, the advent of Artificial Intelligence (AI), Internet of Things (IoT), and data analytics are transforming it. This paper provides a structured outcome-oriented narrative review of the applications of smart technology in two dominant areas: construction management and transportation infrastructure. It is based on 37 peer-reviewed journal articles, conference papers, and technical reports from 2015 to 2026 that were selected purposively, without using a registered systematic search. The evidence has been grouped into eight application themes: AI/ML in construction project management; IoT-enabled site and structural monitoring; AI-driven intelligent transportation systems; IoT-based smart pavement and road monitoring; machine-learning-based structural health monitoring; digital twins and blockchain; UAV and computer-vision monitoring; and cybersecurity in smart infrastructure. The reported outcomes represent distinct performance dimensions and are not directly compared: examples include a 0.12% cost variance for AI/ML project models, a concrete strength prediction error within 1–2 MPa alongside a maximum UWB worker positioning error of 1.44 m for an IoT-based system, a 98.6% traffic-monitoring performance for a machine-learning classification model, and a hardhat-detection precision of 93.1% for Faster R-CNN. Themes of AI and IoT integration are most frequently associated with improved prediction, monitoring, and decision support, while digital twins and blockchain are primarily associated with data trust, traceability, and operational latency reduction. Recurring barriers include data interoperability, data security, implementation costs, and metric heterogeneity across literature. This review concludes with a research agenda prioritizing interoperable data standards, edge-AI and federated learning, cybersecurity-by-design, and standardized outcome reporting.

Authors

Institutions

Publication Details

Journal
International Journal of Applied Smart Interdisciplinary Technologies
Published
2026-10-29
DOI
https://doi.org/10.68104/ijasit.v1.i4.53
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart Technologies in Construction and Transportation Infrastructure: A Review of AI, IoT, and Data-Driven Innovations

Vijay Lalwani
3 links
International Journal of Applied Smart Interdisciplinary Technologies
Infrastructure Maintenance and Monitoring
article

Smart Technologies in Construction and Transportation Infrastructure: A Review of AI, IoT, and Data-Driven Innovations

Vijay Lalwani
article en

Abstract

While the field of civil engineering has traditionally been dominated by hands-on and experience-based techniques, the advent of Artificial Intelligence (AI), Internet of Things (IoT), and data analytics are transforming it. This paper provides a structured outcome-oriented narrative review of the applications of smart technology in two dominant areas: construction management and transportation infrastructure. It is based on 37 peer-reviewed journal articles, conference papers, and technical reports from 2015 to 2026 that were selected purposively, without using a registered systematic search. The evidence has been grouped into eight application themes: AI/ML in construction project management; IoT-enabled site and structural monitoring; AI-driven intelligent transportation systems; IoT-based smart pavement and road monitoring; machine-learning-based structural health monitoring; digital twins and blockchain; UAV and computer-vision monitoring; and cybersecurity in smart infrastructure. The reported outcomes represent distinct performance dimensions and are not directly compared: examples include a 0.12% cost variance for AI/ML project models, a concrete strength prediction error within 1–2 MPa alongside a maximum UWB worker positioning error of 1.44 m for an IoT-based system, a 98.6% traffic-monitoring performance for a machine-learning classification model, and a hardhat-detection precision of 93.1% for Faster R-CNN. Themes of AI and IoT integration are most frequently associated with improved prediction, monitoring, and decision support, while digital twins and blockchain are primarily associated with data trust, traceability, and operational latency reduction. Recurring barriers include data interoperability, data security, implementation costs, and metric heterogeneity across literature. This review concludes with a research agenda prioritizing interoperable data standards, edge-AI and federated learning, cybersecurity-by-design, and standardized outcome reporting.

International Journal of Applied Smart Interdisciplinary Technologies
Parul University (IN)
Industry, innovation and infrastructure, Sustainable cities and communities
Openalex Percentile: Top 19%
Infrastructure Maintenance and Monitoring
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon