The rise in noise and air pollution poses severe risks to human health and the environment.Industrial and vehicular emissions release harmful pollutants such as CO_(2),SO_(2),CO,CH_(4),and noise,leading to significant...The rise in noise and air pollution poses severe risks to human health and the environment.Industrial and vehicular emissions release harmful pollutants such as CO_(2),SO_(2),CO,CH_(4),and noise,leading to significant environmental degradation.Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks.However,existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost,Io'T-based pollution detection system that integrates gas sensors(MQ-135and M(Q-4),a noise sensor(LM393),and a humidity sensor(DHT-22),all connected to a Node MCU(ESP8266)microcontroller.The system leverages cloud-based storage and real-time analytics to monitor harmful gas levels and sound pollution.Sensor data is processed using decision tree algorithms for classification,enabling threshold-based detection with environmental context.A Progressive Web Application(PWA)interface provides tusers with accessible,cross-platform visualizations.Experimental validation demonstrated the system’s ability to detect pollutant concentration variations across both indoor and outdoor settings,with real-time alerts triggered when thresholds were exceeded.The collected data showed consistent classification of normal,warning,and critical states for methane,CO_(2),temperature,humidity,and noise levels.These results confirm the system's reliability in dynamic environmental conditions.The proposed framework offers ascalable,energy-efficient,and user-friendly solution for pollution detectionand public awareness.Future enhancements will focus on extending the sensor suite,improving machine learning accuracy,and integrating meteorological data for predictive pollution modeling.展开更多
The capability of embedded piezoelectric wafer active sensors(PWAS)to perform in-situ nondestructive evaluation(NDE)for structural health monitoring(SHM)of reinforced concrete(RC)structures strengthened with fiber rei...The capability of embedded piezoelectric wafer active sensors(PWAS)to perform in-situ nondestructive evaluation(NDE)for structural health monitoring(SHM)of reinforced concrete(RC)structures strengthened with fiber reinforced polymer(FRP)composite overlays is explored.First,the disbond detection method were developed on coupon specimens consisting of concrete blocks covered with an FRP composite layer.It was found that the presence of a disbond crack drastically changes the electromecfianical(E/M)impedance spectrum lneasurcd at the PWAS terlninals.The spectral changes depend on the distance between the PWAS and the crack tip.Second,large scale experiments were conducted on a RC beam strengthened with carbon fiber reinforced polymer(CFRP)composite overlay.The beam was subject to an accelerated fatigue load regime in a three-point bending configuration up to a total of 807,415 cycles.During these fatigue tests,the CFRP overlay experienced disbonding beginning at about 500,000 cycles.The PWAS were able to detect the disbonding before it could be reliably seen by visual inspection.Good correlation between the PWAS readings and the position and extent of disbond damage was observed.These preliminary results demonstrate the potential of PWAS technology for SHM of RC structures strengthened with FRP composite overlays.展开更多
文摘The rise in noise and air pollution poses severe risks to human health and the environment.Industrial and vehicular emissions release harmful pollutants such as CO_(2),SO_(2),CO,CH_(4),and noise,leading to significant environmental degradation.Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks.However,existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost,Io'T-based pollution detection system that integrates gas sensors(MQ-135and M(Q-4),a noise sensor(LM393),and a humidity sensor(DHT-22),all connected to a Node MCU(ESP8266)microcontroller.The system leverages cloud-based storage and real-time analytics to monitor harmful gas levels and sound pollution.Sensor data is processed using decision tree algorithms for classification,enabling threshold-based detection with environmental context.A Progressive Web Application(PWA)interface provides tusers with accessible,cross-platform visualizations.Experimental validation demonstrated the system’s ability to detect pollutant concentration variations across both indoor and outdoor settings,with real-time alerts triggered when thresholds were exceeded.The collected data showed consistent classification of normal,warning,and critical states for methane,CO_(2),temperature,humidity,and noise levels.These results confirm the system's reliability in dynamic environmental conditions.The proposed framework offers ascalable,energy-efficient,and user-friendly solution for pollution detectionand public awareness.Future enhancements will focus on extending the sensor suite,improving machine learning accuracy,and integrating meteorological data for predictive pollution modeling.
基金the National Seienee Foundation through grants NSF#CMS-9908293 and NSF INT-9904493the Federal Highway Administration and the South Carolina Department of TransPortation(projeet Number 614)
文摘The capability of embedded piezoelectric wafer active sensors(PWAS)to perform in-situ nondestructive evaluation(NDE)for structural health monitoring(SHM)of reinforced concrete(RC)structures strengthened with fiber reinforced polymer(FRP)composite overlays is explored.First,the disbond detection method were developed on coupon specimens consisting of concrete blocks covered with an FRP composite layer.It was found that the presence of a disbond crack drastically changes the electromecfianical(E/M)impedance spectrum lneasurcd at the PWAS terlninals.The spectral changes depend on the distance between the PWAS and the crack tip.Second,large scale experiments were conducted on a RC beam strengthened with carbon fiber reinforced polymer(CFRP)composite overlay.The beam was subject to an accelerated fatigue load regime in a three-point bending configuration up to a total of 807,415 cycles.During these fatigue tests,the CFRP overlay experienced disbonding beginning at about 500,000 cycles.The PWAS were able to detect the disbonding before it could be reliably seen by visual inspection.Good correlation between the PWAS readings and the position and extent of disbond damage was observed.These preliminary results demonstrate the potential of PWAS technology for SHM of RC structures strengthened with FRP composite overlays.
