In recent years, piezoelectric-based transducers and technologies have made significant progress towards structural health monitoring and damage evaluation for various metal and concrete structures. Timber is still commonly used as a construction material in practical engineering; however, there is a lack of research on the health monitoring of timber-based structures using piezoelectric-based transducers and methods. This paper conducts a feasibility study on timber damage detection using surface-mounted piezoelectric patches, which enable the stress-wave-based active sensing approach. Typical damage modes in timber frame structures, such as surface cracks and holes, were investigated in this study. In the active sensing approach, one piezoceramic transducer is used as an actuator to generate stress waves, which propagate along the surface of the timber structure, and other piezoceramic transducers function as sensors to detect the propagating stress waves. Defects, such as a crack or a hole, induce additional attenuation to the propagating stress wave. Based on this attenuation, the proposed method can detect the defects using the wavelet-packet-based damage index, demonstrating its implementation potential for real-time timber damage detection.

The filling of thin-walled steel tubes with quartz sand can help to prevent the premature buckling of the steel tube at a low cost. During an impact, the internal stress of the quartz sand-filled steel tube column is subjected to not only axial force but also lateral confining force, resulting in complicated internal stress. A suitable sensor for monitoring the internal stress of such a structure under an impact is important for structural health monitoring. In this paper, piezoceramic Smart Aggregates (SAs) are embedded into a quartz Sand-Filled Steel Tube Column (SFSTC) to monitor the internal structural stress during impacts. The piezoceramic smart aggregates are first calibrated by an impact hammer. Tests are conducted to study the feasibility of monitoring the internal stress of a structure. The results reflect that the calibration value of the piezoceramic smart aggregate sensitivity test is in good agreement with the theoretical value, and the output voltage value of the piezoceramic smart aggregate has a good linear relationship with external forces. Impact tests are conducted on the sand-filled steel tube with embedded piezoceramic smart aggregates. By analyzing the output signal of the piezoceramic smart aggregates, the internal stress state of the structure can be obtained. Experimental results demonstrated that, under the action of impact loads, the piezoceramic smart aggregates monitor the compressive stress at different locations in the steel tube, which verifies the feasibility of using piezoceramic smart aggregate to monitor the internal stress of a structure.

This paper provides an experimental investigation on the cracking process and residual mechanical properties of concrete after exposure to elevated temperatures. A total of 36 standard concrete prism specimens were tested after exposure to high temperatures of up to 600 °C. The failure modes, cracking process, residual mechanical properties, deformation characteristics and the strain distribution on the surface during the loading procedure were presented. The influences of exposure temperature and water–cement ratio (w/c) were interpreted. The digital image correlation (DIC) method was applied to quantitatively and visually characterize the development of cracking and relative displacement on the concrete surface. The findings suggest that the residual compressive strength and elastic modulus of the concrete decreases gradually with the increasing temperature, especially in the specimens with lower w/c ratio. The DIC technique provides an effective means to measure very precise and detailed information, including the crack opening and distribution of strain on the concrete surface.

In recent years, the piezoceramic transducer-enabled active sensing technique has been extensively applied to structural damage detection and health monitoring, in civil engineering. Being abundant and renewable, timber has been widely used as a building material in many countries. However, one of the more challenging applications of timber, in construction, is the potential damage caused by moisture. Increased moisture may cause easier warping of timber components and encourage corrosion of integrated metal members, on top of potentially causing rot and decay. However, despite numerous efforts to inspect and monitor the moisture content of timber, there lacks a method that can provide truly real time, quantitative, and non-invasive measurement of timber moisture. Thus, the research presented in this paper investigated the feasibility of moisture-content monitoring using an active sensing approach, as enabled by a pair of the Lead Zirconate Titanate (PZT) transducers bonded on the surface of a timber specimen. Using a pair of transducers in an active sensing scheme, one patch generated a designed stress wave, while another patch received the signal. While the active sensing was active, the moisture content of the timber specimen was gradually increased from 0% to 60% with 10% increments. The material properties of the timber correspondingly changed under varying timber moisture content, resulting in a measurable differential in stress wave attenuation rates among the different specimens used. The experimental results indicated that the received signal energy and the moisture content of the timber specimens show a parabolic relationship. Finally, the feasibility and reliability of the presented method, for monitoring timber moisture content, are discussed.

To ensure the safety of construction site personnel and to improve the efficiency of emergency safety evacuation of site personnel, this study analyzes the risk reasons for fire accidents and the characteristics of combustion fires on construction sites. Based on a refined BIM model, a numerical simulation of the fire situation is performed using PyroSim (2019 version) software on a construction site. In the Pyrosim fire simulation model, fire scenarios with distinct construction stages and fire source locations are set up to simulate, compare, and analyze the varying pattern of each fire product in various fire scenarios. Using this information with the Pathfinder (2019 version) simulation model, a coupled simulation test of fire evacuation is conducted to assess the safety of evacuating individuals in each fire scenario. The results show that flammable materials in open spaces are more risky to burn than in confined spaces. After optimizing the utilization of safety exits and the density of people in the second simulation, it was found that the required safety evacuation time was reduced to 267 s, which is lower than the available safety evacuation time of 318.5 s for each scenario. All fire scenarios meet the safe evacuation criteria. The study results can provide a theoretical basis for developing fire response strategies for construction units and contribute to site safety management.

Timber has been commonly used in the field of civil engineering, and the health condition of timber is of great significance for the whole structure in practical scenarios. However, due to mechanical load and environmental impact, timber-based constructions are vulnerable to termite attack, microbial corrosion and fractures within their service lives. Thus, the damage monitoring of timber structures is very challenging under real situations. This paper presents an intelligent timber damage monitoring approach using Lead Zirconate Titanate (PZT)-enabled active sensing and intrinsic multiscale entropy analysis. The proposed approach adopts PZT-enabled active sensing to collect the signals depicting dynamic characteristics of the timber structure. The proposed intrinsic multiscale entropy analysis utilizes variational mode decomposition (VMD) to deal with the collected response signals. Decomposition of the response signals into a set of band-limited intrinsic mode functions (BLIMFs) denoting nonlinear and nonstationary characteristics. Then multiscale sample entropy (MSE) is employed to extract quantitative features, which are adopted as health condition indicators of timber structures. Finally, the convolutional neural network (CNN) fulfills the intelligent timber damage monitoring by using the quantitative features as the effective input. The research findings reveal the efficacy and superiority of the proposed method.