Monitoring of structures’ condition plays a fundamental role in providing safety for users and extending the structures’ lifespan. The monitoring is conducted through on-site inspections by engineers thus this process is time-consuming, labor-intensive and prone to subjective engineering opinions. Detecting damage using machine learning algorithms on images can support engineers’ work, especially for early damages which are difficult to see with the human eye. This article is focused on the concrete crack detection problem in engineering structural elements. Despite the availability of several concrete crack detection datasets, no dataset allows semantic segmentation of cracks narrower than 0.3 mm (the crack width limit for typical engineering structures elements and environmental conditions according to EC 1992-1-1) and the ability for crack classification is limited. The provided open dataset represents only cracks below the crack width limit of 0.3mm, which do not yet indicate concrete elements failure. It is dedicated for early crack classification and segmentation, so that damage protection can be taken at an early stage to prevent structural element damages.

With increasing access to open spatial data, it is possible to improve the quality of analyses carried out in the preliminary stages of the investment process. The extraction of buildings from raster data is an important process, especially for urban, planning and environmental studies. It allows, after processing, to represent buildings registered on a given image, e.g., in a vector format. With an actual image it is possible to obtain current information on the location of buildings in a defined area. At the same time, in recent years, there has been huge progress in the use of machine learning algorithms for object identification purposes. In particular, the semantic segmentation algorithms of deep convolutional neural networks which are based on the extraction of features from an image by means of masking have proven themselves here. The main problem with the application of semantic segmentation is the limited availability of masks, i.e., labelled data for training the network. Creating datasets based on manual labelling of data is a tedious, time consuming and capital-intensive process. Furthermore, any errors may be reflected in later analysis results. Therefore, this paper aims to show how to automate the process of data labelling of cadastral data from open spatial databases using convolutional neural networks, and to identify and extract buildings from high resolution orthophotomaps based on this data. The conducted research has shown that automatic feature extraction using semantic ML segmentation on the basis of data from open spatial databases is possible and can provide adequate quality of results.

Deep machine learning in bridge structures durability analysis

Dynamic tests enable assessment of the structure’s technical condition and provide information necessary for management and maintenance throughout the object’s life cycle. On their basis, the dynamic characteristics of the object are estimated (e.g., the logarithmic decrement). The possible occurrence of atypical features in the obtained signal (e.g. amplitude beat, outliers), as well as the influence of the type of devices and sensors used for measurements, should be considered. If these features are omitted during the analysis, key dynamic characteristics may be evaluated incorrectly. Therefore, this study presents development of a reproducible, universal and robust open-source algorithm for effective estimation of the logarithmic decrement of bridge structures as a reproducible research. Using the presented approach, it is possible to obtain correct results regardless of the signal’s specificity and its atypical features, as well as the type of devices used to collect data in the in-situ conditions. Two approaches based on the use of advanced regression models are considered to estimate the logarithmic decrement. These are direct non-linear approximation (DNAP) and Hilbert non-linear approximation (HNAP). The enriched HNAPsolution was then implemented as a Python module with a "Signal" class and tested on two independent in-situ examples. The presented approach led to effective and correct estimation of the logarithmic decrement, and proved to be insensitive to the type of bridge, its structural characteristics, atypical features of the obtained signal, and the specificity of the data acquisition techniques. In contrast to methods based on deep machine learning, the presented solution does not require a large learning set representative for a given type of design and works independently of the size of the data sample. As demonstrated in the paper, the solution based on the Hilbert transform allows efficient determination of the damping decrement even in the presence of beat frequencies as well as outlier data. The algorithm works independently of the measurement method, with the necessary functions for preprocessing being implemented in the module itself. The solution has been optimized for improved speed, reliability, and reproducibility of results. Doi: 10.28991/CEJ-2022-08-04-02 Full Text: PDF

Inconsistent and erroneous file naming is a recurring information management problem in BIM and Common Data Environment (CDE) workflows, affecting document identification, retrieval, traceability, and control. This problem was observed during work on a real-world modular railway station project, where received documentation included files prepared under heterogeneous naming practices. This paper proposes the Level of Naming Compliance (LoNC), a multi-level framework for assessing file name compliance in BIM/CDE environments. The framework evaluates file names as structured information containers and combines three validation stages: syntactic validation using regular expressions, referential validation against project-specific lookup tables, and LLM-supported semantic validation comparing selected decoded file name fields with descriptive document titles. In the current implementation, semantic validation was limited to discipline and document type. The proposed approach was evaluated using a controlled synthetic validation dataset of 500 file-title pairs, developed from inconsistency types observed in the motivating project. The dataset contained predefined examples of syntactic errors, invalid reference values, semantic inconsistencies, and fully compliant cases. Within this controlled evaluation set, the validator showed full agreement with the predefined expected LoNC levels. Because the dataset contained an unequal distribution of compliance classes, the evaluation was supplemented with balanced accuracy and class-specific performance measures. The results indicate that the implemented workflow correctly operationalises the cumulative LoNC framework under the tested conditions. The findings demonstrate that LoNC provides a more diagnostic assessment than binary file name checking by identifying the stage and type of non-compliance. The approach can support CDE managers and document controllers in quality assurance processes, while future work should validate it on larger real-world datasets and extend semantic validation beyond the selected fields.

This paper describes a software toolbox for LiDAR and LiDAR-Inertial Odometry qualitative and quantitative evaluation. We provide software as https://github.com/MapsHD organization with all necessary information at https://github.com/MapsHD/HDMapping. Our software contributions are a) ground truth data processing tool, b) dockerized state-of-the-art LO and LIO algorithms, c) multi-session data registration to common coordinate system, d) Absolute Pose Error (APE) and Relative Pose Error (RPE) metrics, e) import/export tools for easier 3D data handling and visualizing e.g. in Cloud Compare software. This software is compatible with ROS1 (Robot Operating System) and ROS2 data formats. We shown an example benchmark of LeGO-LOAM, LIO-SAM, FAST-LIO, DLO, VoxelMap, Faster-LIO, KISS-ICP, CT-ICP, SLICT, DLIO, GLIM, iG-LIO, LIO-EKF, I2EKF-LO, GenZ-ICP, RESPLE, odometry_ros_wrapper, Point-LIO, LOAM-Livox algorithms. For all experiments we provide movies.