The present disclosure discloses a method for calculating an internal explosion load speed based on an incremental crack growth distance of a pipeline. The method includes steps of: respectively measuring at least three groups of distances between neighboring markings on forward and backward crack surfaces, and calculating the average values respectively to obtain the average incremental growth distances of forward and backward cracks; calculating the natural vibration frequency of the pipeline; and setting the ratio of backward crack speed to forward crack speed of the pipeline, then calculating the internal explosion load speed of the pipeline by a formula. The present disclosure provides a new effective method for calculating the internal explosion load speed based on the available parameters of the ruptured pipeline after explosion, which can provide a comparatively accurate estimation of internal explosion load speed, thereby providing references for inferring the explosion type occurred in the pipeline.

The present disclosure discloses a method for calculating an internal explosion load speed based on an incremental crack growth distance of a pipeline. The method includes steps of: respectively measuring at least three groups of distances between neighboring markings on forward and backward crack surfaces, and calculating the average values respectively to obtain the average incremental growth distances of forward and backward cracks; calculating the natural vibration frequency of the pipeline; and setting the ratio of backward crack speed to forward crack speed of the pipeline, then calculating the internal explosion load speed of the pipeline by a formula. The present disclosure provides a new effective method for calculating the internal explosion load speed based on the available parameters of the ruptured pipeline after explosion, which can provide a comparatively accurate estimation of internal explosion load speed, thereby providing references for inferring the explosion type occurred in the pipeline.

A method for determining the locations of sensor devices in a sensor array in a structure is disclosed. The method may include placing a sensor device at a location. The method may also include receiving, at a mobile device, an identifier of the sensor device. The method may further include determining, by the mobile device, a compass direction and elevation angle from the sensor device to a base station. The method may additionally include determining a distance between the sensor device and the base station. The method may moreover include determining a location of the sensor device based at least in part on the compass direction and the elevation angle from the sensor device to the base station, and the distance between the sensor device and the base station. The method may furthermore include storing the location of the sensor in association with the identifier of the sensor device.

A method for determining the locations of sensor devices in a sensor array in a structure is disclosed. The method may include placing a sensor device at a location. The method may also include receiving, at a mobile device, an identifier of the sensor device. The method may further include determining, by the mobile device, a compass direction and elevation angle from the sensor device to a base station. The method may additionally include determining a distance between the sensor device and the base station. The method may moreover include determining a location of the sensor device based at least in part on the compass direction and the elevation angle from the sensor device to the base station, and the distance between the sensor device and the base station. The method may furthermore include storing the location of the sensor in association with the identifier of the sensor device.

An inspection apparatus comprises a chassis position/attitude estimator to estimate position/attitude information of a moving body and generate a chassis position/attitude estimation signal, a hammering tester hammer part error signal generator to generate a hammering tester hammer part error signal, a hammering tester hammer part position/attitude signal generator to generate a hammering tester hammer part position/attitude signal, a first sensor data frequency characteristic interpolator to generate a first sensor data frequency characteristic interpolation signal from the received chassis position/attitude estimation signal, a second sensor data frequency characteristic interpolator to generate a second sensor data frequency characteristic interpolation signal from the received hammering tester hammer part error signal and the received hammering tester hammer part position/attitude signal, and a hammering tester hammer part position/attitude estimator to generate a hammering tester hammer part position/attitude estimation signal from the received first sensor data frequency characteristic interpolation signal and the received second sensor data frequency characteristic interpolation signal.

An inspection apparatus comprises a chassis position/attitude estimator to estimate position/attitude information of a moving body and generate a chassis position/attitude estimation signal, a hammering tester hammer part error signal generator to generate a hammering tester hammer part error signal, a hammering tester hammer part position/attitude signal generator to generate a hammering tester hammer part position/attitude signal, a first sensor data frequency characteristic interpolator to generate a first sensor data frequency characteristic interpolation signal from the received chassis position/attitude estimation signal, a second sensor data frequency characteristic interpolator to generate a second sensor data frequency characteristic interpolation signal from the received hammering tester hammer part error signal and the received hammering tester hammer part position/attitude signal, and a hammering tester hammer part position/attitude estimator to generate a hammering tester hammer part position/attitude estimation signal from the received first sensor data frequency characteristic interpolation signal and the received second sensor data frequency characteristic interpolation signal.

Described herein are systems and methods based on acoustic emission (AE) technology to monitor a concrete structure for a short interval and, based on signals acquired, estimate Alkali-silica reaction (ASR) progression status in the structure remotely and efficiently without halting any serviceability and operational activities of the structure, knowing the ASR progression status of the structure helps determine rehabilitation and future structural safety and serviceability of the structure.

Described herein are systems and methods based on acoustic emission (AE) technology to monitor a concrete structure for a short interval and, based on signals acquired, estimate Alkali-silica reaction (ASR) progression status in the structure remotely and efficiently without halting any serviceability and operational activities of the structure, knowing the ASR progression status of the structure helps determine rehabilitation and future structural safety and serviceability of the structure.

A state estimation apparatus 1 includes an acquisition unit 2 that acquires deterioration information indicating a deterioration state of each structural object and a learning unit 3 that learns common information that is common between pieces of the deterioration information and estimation index information that is used for estimating a deterioration state of a target structural object, using the deterioration information as input.

A method for classifying a glass object via acoustic analysis by a classifying apparatus is provided. The method including: receiving, by a processor, sound data of a knock sound generated by applying a knocking operation on the glass object; determining, by the processor, a type of the glass object by performing a knock-sound analysis to the sound data, wherein the type of the glass object includes an organic glass and an inorganic glass; if the type of the glass object is determined as the inorganic glass, receiving, by the processor, echo data of an echo induced by applying an ultrasonic-echo operation on the glass object; and determining, by the processor, a further type of the glass object by performing an echo-decay analysis to the echo data, wherein the further type of the glass object includes a crystal glass, a borosilicate glass and a soda-lime glass.