In line with the joint implementation guidelines on integrating artificial intelligence into the transport sector, issued by seven government bodies, a nationwide call has been launched to gather exemplary applications of AI in highway and waterway construction quality testing. The initiative aims to accelerate the fusion of intelligent technologies within this critical field.

The primary objective is to spotlight AI-driven innovations across all stages of construction quality assessment, selecting cases that demonstrate exceptional technical merit, proven practical impact, and strong scalability. By showcasing solutions that effectively tackle persistent industry challenges, the program seeks to distill replicable models and best practices that can drive digital and intelligent upgrades throughout the sector, fostering technological progress and high-quality development.

The solicitation is open to industry regulators, quality testing institutions, universities, research bodies, professional associations, and technology enterprises. Eligible submissions must feature AI applications in highway and waterway construction quality testing, with emphasis on the following categories:

Intelligent Inspection and Dynamic Sensing

This category covers the development and deployment of AI visual inspection systems, multi-modal sensor fusion, intelligent inspection robots, embedded smart systems, and cloud-edge-device collaborative platforms. The focus is on achieving direct IoT transmission of inspection data, enhancing overall efficiency and intelligent coverage, reducing operational risks, and strengthening quality assurance safeguards.

Data Analytics and Intelligent Assessment

Building on inspection data, this category highlights innovations in intelligent defect and outcome recognition, edge computing for real-time assessment, multi-source sensor data fusion and diagnosis, and quality risk early-warning mechanisms. The goal is to overcome challenges such as inefficient data processing, inaccurate defect detection, and delayed forecasting, thereby improving diagnostic precision and proactive risk management.

Intelligent Workflow Optimization for Inspection Processes

This area focuses on smart sampling and optimized inspection planning, collaborative multi-agent operations, full-process on-site inspection automation, automated report generation and review, intelligent process tracing, compliant on-site behavior management, and robust data provenance. These innovations aim to standardize field practices, minimize human intervention, boost execution efficiency, reinforce compliance oversight, and ensure end-to-end traceability.

Smart Laboratory Management

This category pertains to comprehensive intelligent control of laboratory elements, the establishment of full-chain quality control systems, lifecycle-based quality tracing frameworks, and digital laboratory operations. The objective is to elevate laboratory performance while building credible data management and complete traceability mechanisms.

Regulatory Empowerment for Industry Oversight

This final category addresses non-site intelligent supervision, anomaly detection and analysis of testing institutions, cross-regional penetrating oversight, and intelligent decision support for macro-level industry governance. These applications are designed to overcome the limitations of traditional supervision—such as lag, insufficient coverage, and slow response—thereby enabling regulators to conduct smart monitoring and off-site enforcement, enhancing governance effectiveness, and accelerating transformative management improvements.

Regarding procedural requirements, the Ministry will centrally coordinate, collect, and select submissions, with provincial transport authorities encouraged to actively promote and organize applications within their jurisdictions. Notably, this initiative does not involve naming, certification, or any form of demonstration or evaluation activities. Applicants must comply with all relevant laws, regulations, and special supervisory requirements, ensuring their materials are free of confidential, infringing, or inappropriate content. Submitted cases should represent real, implemented projects with clearly defined stakeholders, timelines, and verifiable outcomes.

Applicants are expected to identify typical application scenarios aligned with their actual quality testing work and the specified categories. Selected cases should demonstrate advancement through the use of state-of-the-art AI technologies and industry-leading positions; originality through independently developed intellectual property that addresses common sector challenges or initiates transformative work models; and leadership through mature, replicable application scenarios with broad promotion value. Submissions should include quantifiable and verifiable results, specifying statistical methodologies, scope of application, and validation approaches as detailed in the application form. Entities must avoid duplicate submissions across categories, with a maximum of five cases per applicant. For joint applications, the lead organization bears responsibility for the authenticity of materials, data compliance, and result reproducibility.

Applications must be completed using the designated form and submitted via email with both stamped scanned and Word versions to the specified address by September 4. Contact details for the responsible person must be included. For inquiries, please call 010-65292793 or email jica@mot.gov.cn.