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In order to improve the comprehensive evaluation level of shield tunnel structure health, taking a subway tunnel section as an example, and combined with the onsite measured data, such as regular inspection, health monitoring and disease remediation, this paper introduces the variable weight theory to improve the traditional fixed-weight evaluation method from structural deformation, current durability and disease status.

Considering the influence of the fluctuation of each index value on the index weight, a comprehensive structural health evaluation model of shield tunnel based on an improved variable weight matter-element extension model is proposed.

Compared with the traditional fixed-weight evaluation method, this model can correct the evaluation distortion caused by the fluctuation of index value and has optimal effect.

The sensitive analysis shows that several key indicators of the main threats to tunnel structure are obtained to improve the efficiency of operation, maintenance and management of shield tunnel structure.

The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.

A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.

The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.

The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.

A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

This study aims to present a method to carry out technological foresight exercises in university technological research and development centers (TRDCs). The results support the organizational planning process.

The authors review the literature on the frameworks of foresight. The authors propose the use of quantitative (bibliometrics) and qualitative (expert meeting, Delphi) techniques. The authors apply this method to each research topic of interest of the TRDCs.

The application of the method to a research topic allows us to analyze the relevant knowledge areas for the development of that topic and its global trend. For each area, the authors identify the specific lines of research with the greatest growth and relevance. Considering these lines, the authors make a diagnosis of the TRDC situation regarding how to provide knowledge to decision-makers.

The paper reviews the results of the application in the Engineering Institute of the National Autonomous University of Mexico and in the research topic “Seismic risks in civil engineering.” It is necessary to adjust the application of the method in other TRDCs to the conditions of the organization and to the development achieved by the TRDCs in the analyzed research topic.

There is no detailed literature on technological foresight exercises in university TRDCs that support the planning process of the organization. The method represents a proposal to establish priority research topics and specific research lines in research institutes, particularly universities. The authors present the application in a university TRDCs in Mexico.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.