Automated classification of civil structures defects based on Convolutional Neural Network

Today, the most used method for civil infrastructure inspection is based on visual assessment performed by certified inspectors following prescribed protocols. However, the increase in aggressive environmental and load conditions, coupled with the achievement for many structures of the end life-cycle, highlighted the need to automate damage identification to satisfy the number of structures that need to be inspected. To overcome this challenge, the current paper presents a method to automate the concrete damage classification using a deep Convolutional Neural Network (CNN). The CNN is designed after an experimental investigation among a wide number of pretrained networks, all applying the transfer learning technique. Training and Validation are performed using a built database with 1352 images balanced between “undamaged”, “cracked”, and “delaminated” concrete surface. To increase the network robustness compared to images with real-world situations, different configurations of images has been collected from Internet and on-field bridge inspections. The GoogLeNet model is selected as the most suitable network for the concrete damage classification, having the highest validation accuracy of about 94%. The results confirm that the proposed model can correctly classify images from real concrete surface of bridges, tunnel and pavement, resulting an effective alternative to the current visual inspection

Identification of a selective G1-phase benzimidazolone inhibitor by a senescence-targeted virtual screen using artificial neural networks

Cellular senescence is a barrier to tumorigenesis in normal cells and tumour cells undergo senescence responses to genotoxic stimuli, which is a potential target phenotype for cancer therapy. However, in this setting, mixed-mode responses are common with apoptosis the dominant effect. Hence, more selective senescence inducers are required. Here we report a machine learning-based in silico screen to identify potential senescence agonists. We built profiles of differentially affected biological process networks from expression data obtained under induced telomere dysfunction conditions in colorectal cancer cells and matched these to a panel of 17 protein targets with confirmatory screening data in PubChem. We trained a neural network using 3517 compounds identified as active or inactive against these targets. The resulting classification model was used to screen a virtual library of ~2M lead-like compounds. 147 virtual hits were acquired for validation in growth inhibition and senescence-associated β-galactosidase (SA-β-gal) assays. Among the found hits a benzimidazolone compound, CB-20903630, had low micromolar IC50 for growth inhibition of HCT116 cells and selectively induced SA-β-gal activity in the entire treated cell population without cytotoxicity or apoptosis induction. Growth suppression was mediated by G1 blockade involving increased p21 expression and suppressed cyclin B1, CDK1 and CDC25C. Additionally, the compound inhibited growth of multicellular spheroids and caused severe retardation of population kinetics in long term treatments. Preliminary structure-activity and structure clustering analyses are reported and expression analysis of CB-20903630 against other cell cycle suppressor compounds suggested a PI3K/AKT-inhibitor-like profile in normal cells, with different pathways affected in cancer cells

Software toolset to enable image classification of earthquake damage to above-ground infrastructure

A critical task after a significant earthquake is determining the extent of damage to infrastructure networks. The decision-making process to dispatch emergency, repair, and in-field reconnaissance teams depends on whether road/railways and bridges are passible. Another concern is the rapid identification and resolution of physical disruptions to large-volume gas and water pipeline systems. After large seismic events, citizens, amateur photographers, and journalists now post thousands of photographs to formal/social media platforms. In the past, these images would have had to be reviewed by trained volunteers or expert engineers to evaluate whether: road/rail ways were significantly impacted by ground fracture, heaving, slope failure, or rock slides; bridges experienced severe damage or partial-to-complete collapse; and pipeline systems were interrupted by differential ground movement or liquefaction. The manual review of large imagesets for assessing damage has shown to be inefficient and, in cases, error-prone. This paper presents an automatic and rapid approach, based on computer vision techniques, to assessing damage to above-ground infrastructure networks via images uploaded in the immediate aftermath of the earthquake. The authors developed an algorithm based on deep learning (DL) that automatically tags images. Progress to date shows the algorithm correctly assigns individual tags to 92% of roadway images exhibiting cracking (of varying directionality and severities) and 80% of railways affected by horizontal offset (lateral translation). These results show promise and future research efforts entail tagging both of the aforementioned damage types in a single image

Structural damage identification of beam structures using two stage method based on modal strain energy indicators and artificial neural networks

Engineering structures are subjected to various types of environmental exposure conditions and other loadings during their entire service life. There is a possibility of degradation of their strength and damage in the critical locations. Monitoring their health as well as assessment of the damage and safety evaluation of structures is gaining high importance. In view of locating and quantifying the damage in structures, a two stage methodology is proposed in this paper. Appropriate finite element models of beams are developed. Initially, the modal strain energy based damage indices are evaluated. The normalized mode shapes as well as normalized curvature mode shapes are used for computation of damage indices. Later, the severity of damage is predicted by using Artificial Neural Networks. The performance of modal strain energy based indices is evaluated for various damage cases with different locations and severities. The performance of the proposed method is assessed for cantilever, simply supported and fixed support conditions

Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration