Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.

Deterioration Mechanisms and Advanced Inspection Technologies of Aluminum Windows

Aluminum windows are crucial components of building envelopes since they connect the indoor space to the external environment. Various external causes degrade or harm the functioning of aluminum windows. In this regard, inspecting the performance of aluminum windows is a necessary task to keep buildings healthy. This review illustrates the deterioration mechanisms of aluminum windows under various environmental conditions with an intention to provide comprehensive information for developing damage protection and inspection technologies. The illustrations reveal that moisture and chloride ions have the most detrimental effect on deteriorating aluminum windows in the long run, while mechanical loads can damage aluminum windows in a sudden manner. In addition, multiple advanced inspection techniques potential to benefit assessing aluminum window health state are discussed in order to help tackle the efficiency problem of traditional visual inspection. The comparison among those techniques demonstrates that infrared thermography can help acquire a preliminary defect profile of inspected windows, whereas ultrasonic phased arrays technology demonstrates a high level of competency in analyzing comprehensive defect information. This review also discusses the challenges in the scarcity of nanoscale corrosion information for insightful understandings of aluminum window corrosion and reliable window inspection tools for lifespan prediction. In this regard, molecular dynamics simulation and artificial intelligence technology are recommended as promising tools for better revealing the deterioration mechanisms and advancing inspection techniques, respectively, for future directions. It is envisioned that this paper will help upgrade the aluminum window inspection scheme and contribute to driving the construction of intelligent and safe cities.

On the assessment of non-metallic inclusions by part 13 of API 579 -1/ASME FFS-1 2016

Improvement of nondestructive inspection techniques has allowed more frequent detection of closely spaced zones of non-metallic inclusions in pressure vessels made of low carbon steel. In the present study, closely spaced inclusions in an in-service cylindrical horizontal pressure vessel were detected by Scan-C ultrasonic inspection and considered as laminations to be assessed by Part 13 of the API 579-1/ASME FFS-1 2016 standard. The outcoming results were considered as a rejection for Level 1 assessment, and a repair or replacement of the component was required, even though it retained a significant remaining strength. Thus, an alternative procedure to assess the mechanical integrity of pressure vessels containing zones of non-metallic inclusions is proposed by adopting some criteria of the API 579-1/ASME FFS-1 Part 13 standard procedure and taking into consideration the dimensions and grouping characteristics of the inclusion zones.

Optimized Inspection and Integrity Assessment of Well Conductors for Life Extension Strategic Planning

Assuring integrity of offshore well Conductor is one of the challenges in the aged giant offshore fields operating with 1500+ wells. Such fields should have a robust and efficient integrity management system for inspection and assessment of well conductors through the well life cycle. Offshore well Conductors form the secondary load-bearing element in a well, primary being the surface casing. A practical approach in assessing the structural integrity of the well conductor is proposed in this paper. Wells were classifying into five subgroups; optimized Inspection and Integrity Assessment methods used to establish the structural integrity of conductors; which were evaluated and validated by a 3rd part consultant. The assessment results indicate how over-conservative assumptions in engineering assessment may mislead operators to categorize wells into higher risk. Assessment was performed utilizing various modeling software. Reliability based approach was adopted to accommodate uncertainties in data utilizing appropriate engineering judgement to tackle data gaps. Average thickness measured at discrete elevations was compared with the calculated minimum required thickness (MRT) to assess the structural integrity status of conductors. This approach helped in the decision making and planning for risk mitigation repairs. The results of optimized inspection techniques and structural assessment methodology lead to establishment of clear pattern for critical well conductors and applied to the groups to decide on maintenance strategy. The conductor wall thickness data measured from automated thickness measurement technique is matching with the measured data from manual thickness measurements. The average wall thickness at each elevation, obtained from the raw automated thickness measurement technique data to be used for assessment of the conductor. After building good confidence in the mode of failure the results indicated that manual thickness measurement technique is sufficient to assess the structural integrity of the conductors. The consultant has performed parametric studies to validate the Minimum Required Thickness (MRT) for the most onerous well in the group; by modelling the boundary conditions of conductor span between the guides, the critical water depth, well depth etc. Sensitivity studies were performed considering the environmental loading due to wind, wave, current; vortex induced vibrations, cement height behind the pipes etc. From the new findings the integrity status of the current wells risk ranking will be reviewed and if the average measured thickness is greater than the MRT then a repair program is no more required. The resource utilization was optimized based on the final outcome of the exercise. A procedure based optimized inspection techniques and structural integrity assessments to the group the well conductors resulted in the revision of risk ranking of wells, efficient maintenance planning and achieve high-cost optimization for its life extension. The outcome of the consultancy study will also substantiate our current method of conductor assessment and decision for repair based on risk-based approach. Based on the learnings this paper will be focusing on utilizing optimal inspection and assessment approach.

Aerial Robotic Solution for Detailed Inspection of Viaducts

The inspection of public infrastructure, such as viaducts and bridges, is crucial for their proper maintenance given the heavy use of many of them. Current inspection techniques are very costly and manual, requiring highly qualified personnel and involving many risks. This article presents a novel solution for the detailed inspection of viaducts using aerial robotic platforms. The system provides a highly automated visual inspection platform that does not rely on GPS and could even fly underneath the infrastructure. Unlike commercially available solutions, our system automatically references the inspection to a global coordinate system usable throughout the lifespan of the infrastructure. In addition, the system includes another aerial platform with a robotic arm to make contact inspections of detected defects, thus providing information that cannot be obtained only with images. Both aerial robotic platforms feature flexibility in the choice of camera or contact measurement sensors as the situation requires. The system was validated by performing inspection flights on real viaducts.

The Snooker Algorithm for Ultrasonic Imaging of Fatigue Cracks in order to use Parameter-Spaces to Aid Machine Learning

Fatigue cracks in a wide array of industrial components and structures pose a significant threat to their integrity. Detecting fatigue cracks using ultrasonic inspection techniques is a widespread activity for economic reasons but there are limitations to the techniques due to the morphology of fatigue cracks. In addition to detection there is a need to measure the size of the cracks which are often within the volume of the material. Ultrasonic techniques are well-suited to look inside the volume of the material but achieving sufficient sensitivity to the tip of the cracks in particular is practically difficult. Without an accurate knowledge of where the tip of the crack lies there can be significant uncertainty in sizing measurements. Machine Learning (ML) techniques are being developed to aid in the inspection and monitoring tasks but presenting the ultrasonic data in a suitable way for machine learning is very important. This paper presents a new approach to condition the ultrasonic data for machine learning settings so that they can be used effectively and confidently to detect and size fatigue cracks. The new approach, using images termed parameter-spaces, will also aid in conventional inspections as they are able to give information to human operators as to the existence or not of these very dangerous cracks.

Online Distributed Measurement of Dark I-V Curves in Photovoltaic Plants

The inspection techniques for defects in photovoltaic modules are diverse. Among them, the inspection with measurements using current–voltage (I-V) curves is one of the most outstanding. I-V curves, which can be carried under illumination or in dark conditions, are widely used to detect certain defects in photovoltaic modules. In a traditional way, these measurements are carried out by disconnecting the photovoltaic module from the string inside the photovoltaic plant. In this work, the researchers propose a methodology to perform online dark I-V curves of modules in photovoltaic plants without the need of disconnecting them from the string. For this, a combination of electronic boards in the photovoltaic modules and a bidirectional inverter are employed. The results are highly promising, and this methodology could be widely used in upcoming photovoltaic plants.