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Few of the established risk identification methods refer to low-severity yet high-frequency safety risks data that may lead to several safety risks being ignored, thus reducing the potential of learning from a considerable number of cases. The purpose of this study is to explore a new valid method based on preaccident safety supervision data to identify these minor construction safety risks during routine construction operations.

A total of 329 official construction safety supervision reports containing 5,159 safety problem records from Shanghai between 2016 and 2018 served as raw material for in-depth analysis. Given the characteristics of the data collected, text mining integrated with natural language processing was applied to review the supervision reports and group safety risks automatically.

This study clarifies the way in which the supervision data should be employed to analyze high-frequency–low-severity safety risks. From these data, seven unsafe-act-related and nine unsafe-condition-related risks are identified. Regarding unsafe-act-related risks, inappropriate human behaviors could usually occur in personnel management, contract management, expense management, material management and acceptance work. For unsafe-condition-related risks, hoisting, scaffolding and reinforcement works are the main generators of onsite safety hazards during construction operations.

The study includes implications for project managers and supervisors to facilitate more effective proactive risk management by paying more attention to collecting and employing the supervision data established in each routine inspection.

Whereas previous research focused on analyzing severe accidents, this study seeks to identify the high-frequency–low-severity construction safety risks using the preaccident supervision data. The findings could provide a new thought and research direction for construction safety risk management.

This paper aims to propose the need for soft and flexible sensors that actually measure the turning angle and torque of a joint. Conventional rigid angular/torque sensors have compatibility issues in wearable applications due to its bulkiness, non-compliance and high rigidity.

The sensing element of the sensor is based on carbon black (CB)/Ecoflex composite, deposited via extrusion printing technique. A simple finite element analysis was used to explain the non-linearity and non-symmetricity behaviours of the sensor.

This prototype can measure the angular rotation up to ±180° and a maximum torque value of 0.6 Nm. The geometry of the printed CB/Ecoflex composite as piezoresistive trace has a significant effect on the output (resistance change) response.

This research explored an extrusion printing techniques that allow customization to construct a soft piezoresistive strain sensor, which can be used as an angular/torque sensor.