Predicting compressive strength of hollow concrete prisms using machine learning techniques and explainable artificial intelligence (XAI).

The design of masonry structures requires accurate estimation of compressive strength (CS) of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by destructive laboratory testing which results in time and resource wastage. Thus, this study aims to provide machine learning-based predictive models for CS of hollow concrete masonry blocks using different algorithms including Multi Expression Programming (MEP), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGB) etc.

Robust Construction Safety System (RCSS) for Collision Accidents Prevention on Construction Sites.

A proximity warning system to detect the presence of a worker/workers and to warn heavy equipment operators is highly needed to prevent collision accidents at construction sites. In this paper, we developed a robust construction safety system (RCSS), which can activate warning devices and automatically halt heavy equipment, simultaneously, to prevent possible collision accidents. The proximity detection of this proposed system mainly relies on ultra-wideband (UWB) sensing technologies, which enable instantaneous and simultaneous alarms on (a) a worker's personal safety (personal protection unit (PPU)) device and (b) hazard area device (zone alert unit (ZAU)).

Hybrid Blockchain and Internet-of-Things Network for Underground Structure Health Monitoring.

The Internet-of-things (IoT) and blockchain are growing realities of modern society, and both are rapidly transforming civilization, either separately or in combination. However, the leverage of both technologies for structural health monitoring (SHM) to enable transparent information sharing among involved parties and autonomous decision making has not yet been achieved. Therefore, this study combines IoT with blockchain-based smart contracts for SHM of underground structures to define a novel, efficient, scalable, and secure distributed network for enhancing operational safety.

Development of a Cloud Computing-Based Pier Type Port Structure Stability Evaluation Platform Using Fiber Bragg Grating Sensors.

Structure Health Monitoring is a topic of great interest in port structures due to the ageing of structures and the limitations of evaluating structures. This paper presents a cloud computing-based stability evaluation platform for a pier type port structure using Fiber Bragg Grating (FBG) sensors in a system consisting of a FBG strain sensor, FBG displacement gauge, FBG angle meter, gateway, and cloud computing-based web server. The sensors were installed on core components of the structure and measurements were taken to evaluate the structures.

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction.