Электронный журнал «ГеоИнфо» - GeoInfo. Vol. VIII, No. 3 (2025)

Abstract: The article examines the structure, systematics, and diversity of ecologicalgeological systems of transport-and-communication complexes – some of the most important components of the technosphere, which perform the functions of transporting various goods and people, conveying mineral resources, and transmitting energy and information, and which are considered a specific type of ecosystems. The paper analyzes the characteristic ecological-geological features of their abiotic and biotic components, as well as the interrelations between them, which must be taken into account during environmental engineering surveys and investigations conducted in areas where transport-and-communication complexes are located.

Keywords: ecological-geological system (EGS); transport-and-communication complexes;

structure; systematics

Abstract: This article presents an overview of modern methods for using video logging (TV logging) to study karst formations and assess the karst hazard of areas. It examines the technical aspects of video logging, its advantages, and limitations in exploring karst cavities. The paper also presents practical experience in using this method to assess karst hazards in the central part of Kazan. It is shown that video logging in karst-prone areas makes it possible to assess the condition of а soil body, identify the location and dimensions of cavities more accurately, which contributes to improving the safety of construction and operation of engineering structures.

Keywords: video logging; karst; karst cavities; engineering-geological survey; geophysical methods; Kazan

Abstract: Organic matter present in soils includes both organic residues that have partially preserved their original structure and individual organic compounds of a specific and non-specific nature. In agricultural reclamation, complete fractionation of organic substances is carried out, whereas for geotechnical purposes, only the total organic content is essential. This is an important classification feature; therefore, the methodology for determining organic matter in soils is given increased attention. However, while in soil science the new editions of GOST standards remain within the framework of classical methods recognized by the scientific community, in engineering and geological surveys, and subsequently in soil science, there is a clear tendency to simplified definitions related mainly to pyrolysis of organics at different temperatures. Is it any wonder that as a result of such manipulations, new lithological types of soils such as “peaty marl” appear?

Keywords: organic matter; undecomposed plant residues; humus; non-specific organic compounds; dry combustion; I.V. Tyurin's method; modifications of Tyurin’s method; pyrolysis of organic matter at different temperatures

Abstract: We bring to the attention of our readers a slightly abridged and adapted translation of the report by primarily Indian geological and geotechnical engineers “Digging deeper: the role of big data analytics in geotechnical investigations” (Vani et al., 2024), which was presented at the 3rd International Conference on Civil, Structural and Environmental Engineering (ICS-MEE) held on 2–3 May 2024 in the Indian city of Kottayam, Kerala. That event was organized by the Mangalam College of Engineering, received financial support from the Ministry of Science and Technology of India, and gathered more than 1,000 participants from all over the world. In the same year, the proceedings of that conference were published in peer-reviewed conference proceedings series “E3S Web of Conferences” issued by the French publishing house “EDP Sciences” (“Edition Diffusion Presse Sciences”). The paper on the basis of that report by Indian specialists is available in open access under the CC BY 4.0 license that allows it to be distributed, translated, adapted, and supplemented, provided that the types of changes, original source, and DOI are noted. In our case, the full reference to the original paper (Chen et al., 2020), which was used for the presented translation, is given in the end.

This review paper explores the transformative role of big data analytics in geotechnical engineering, transferring past conventional methods to a data-driven paradigm that complements decision-making and precision in subsurface investigations. By integrating large statistics analytics with geotechnical engineering, this study demonstrates big improvements in site characterization, danger assessment, and production methodologies. The research underscores the capability of big data to revolutionize geotechnical investigations through improved prediction models, threat management, and sustainable engineering practices, highlighting the critical role of big data in addressing international warming and ozone depletion. Through the examination of numerous case studies and AI-driven methodologies, this paper sheds light at the efficiency gains and environmental benefits attainable in geotechnical engineering.

Keywords: geotechnical engineering; big data; artificial intelligence; machine learning

Abstract: We bring to the attention of our readers a slightly abridged and adapted translation of the report by primarily Indian geological and geotechnical engineers “Digging deeper: the role of big data analytics in geotechnical investigations” (Vani et al., 2024), which was presented at the 3rd International Conference on Civil, Structural and Environmental Engineering (ICS-MEE) held on 2–3 May 2024 in the Indian city of Kottayam, Kerala. That event was organized by the Mangalam College of Engineering, received financial support from the Ministry of Science and Technology of India, and gathered more than 1,000 participants from all over the world. In the same year, the proceedings of that conference were published in peer-reviewed conference proceedings series “E3S Web of Conferences” issued by the French publishing house “EDP Sciences” (“Edition Diffusion Presse Sciences”). The paper on the basis of that report by Indian specialists is available in open access under the CC BY 4.0 license that allows it to be distributed, translated, adapted, and supplemented, provided that the types of changes, original source, and DOI are noted. In our case, the full reference to the original paper (Chen et al., 2020), which was used for the presented translation, is given in the end.

This review paper explores the transformative role of big data analytics in geotechnical engineering, transferring past conventional methods to a data-driven paradigm that complements decision-making and precision in subsurface investigations. By integrating large statistics analytics with geotechnical engineering, this study demonstrates big improvements in site characterization, danger assessment, and production methodologies. The research underscores the capability of big data to revolutionize geotechnical investigations through improved prediction models, threat management, and sustainable engineering practices, highlighting the critical role of big data in addressing international warming and ozone depletion. Through the examination of numerous case studies and AI-driven methodologies, this paper sheds light at the efficiency gains and environmental benefits attainable in geotechnical engineering.

Keywords: geotechnical engineering; big data; artificial intelligence; machine learning

Abstract: We present to the readers a slightly abridged and adapted translation of the article “Expedite quantification of landslides using wireless sensors and artificial intelligence for data controlling practices” by Indian and Saudi Arabian researchers. This work was published in the peer-reviewed journal “Computational Intelligence and Neuroscience” by the Hindawi Publishing Corporation under the CC BY 4.0 license. This license permits copying and distributing the article in any medium and format, adapting, modifying, and creating new works based on it for any purpose, including commercial use, provided that the original source is referenced. In our  case, the full reference to the original source is provided at the end of the translation.

The power of wireless network sensor technologies has enabled the development of large-scale in-house monitoring systems. The sensor may play a big part in landslide forecasting where the sensor linked to the WLAN protocol can usefully map, detect, analyze, and predict landslide distant areas, etc. A wireless sensor network (WSN) comprises autonomous sensors geographically dispersed for monitoring physical or environmental variables, comprising temperature, sound, pressure, etc. This remote management service contains a monitoring system with more information and helps the user grasp the problem and work hard when WSN is a catastrophic event tracking prospect.

This paper illustrates the effectiveness of Wireless Sensor Networks and artificial intelligence (AI) algorithms (i.e., Logistic Regression) for landslide monitoring in real-time. The WSN system monitors landslide causative factors such as precipitation, Earth moisture, pore-water-pressure, and motion in real-time. The problems associated with land life surveillance and the context generated by data are given to address these issues. The WSN and AI give the option of monitoring fast landslides in real-time conditions. A proposed system in this paper shows real-time monitoring of landslides to preternaturally inform people through an alerting system to risky situations.

Keywords: landslides; landslide hazard; operational landslide monitoring; landslide prediction; wireless sensors; wireless sensor network; artificial intelligence; machine learning; logistic regression; support vector machine; stochastic gradient descent

Abstract: We present to the readers an adapted translation of the article “The application of artificial intelligence in geotechnical investigation” by Chinese researchers. This work was published electronically in the collection of scientific papers “Advances in Artificial Intelligence, Big Data and Algorithms” by the international publisher “IOS Press”. It is available in open access under the CC BY-NC 4.0 license, which allows copying and redistribution of the article, adaptation, modification, and creation of new works based on it, but not for commercial purposes, provided that the license type, changes made are indicated and the original source is referenced. In our case, the full reference to the original source is provided at the end of the translation.

The standard penetration test (SPT) and dynamic probing test (DPT) are commonly used exploration methods in geotechnical investigation. However, errors can occur during data collection, often attributed to factors such as human error. To mitigate this issue, this paper proposes the utilization of an improved YOLOv5 object detection algorithm, a form of artificial intelligence technology, to automatically count the number of hammer strikes during geotechnical investigations. The proposed approach incorporates several enhancements to the YOLOv5 network architecture. Firstly, a focal loss function is introduced to address sample imbalance, ensuring better handling of different classes of hammer strikes. Additionally, online hard example mining technology is employed to improve model accuracy by focusing on challenging samples that are most informative for training. The improved YOLOv5 model is then applied to detect hammer strikes in SPT and DPT tests. To facilitate training and evaluation, a hammer detection dataset is created, tailored to the specific requirements of geotechnical investigation. Experimental results demonstrate the superior performance of the proposed improved YOLOv5 object detection model on the hammer detection dataset.

Keywords: geotechnical investigation; field dynamic tests of soils; SPT method; DPT method; artificial intelligence; neural network; YOLOv5 algorithm; hammer detection; hammer strikes detection; model training; online hard example mining; number of hammer strikes; automated counting

Abstract: This paper by our special correspondents, who are also employees of a major development company, addresses a long-standing problem: attempts to save money on engineering surveys and the lack of proper quality control by investors inevitably lead to significant project cost increases. Based on the analysis of case studies from various regions, the authors demonstrate that a reasonable increase in survey costs can pay off many times over through accurate work volume estimation and the prevention of potential risks. The key to quality is not a high price by itself, but a well-prepared technical assignment, the selection of reliable contractors, and strict quality control of survey results, the main criteria for which are their validity, required level of detail, and alignment with project needs.

Key words: site investigation; engineering survey, engineering-geological survey; contractor; expenses; cost; price; quality

Abstract: On September 17, 2025, a round table titled “On the Preservation and Use of Small Rivers under Low-Water Conditions” was held in Rostov-on-Don. The event was organized by the Southern Federal University (SFedU) and the Ministry of Natural Resources and Environment of the Rostov Region. Participants included scientists from SFedU and the Southern Scientific Center of the Russian Academy of Sciences (SSC RAS), other experts in hydrology, ecology, and climatology, as well as representatives of the authorities of the Rostov Region, the Luhansk and Donetsk People’s Republics, and relevant specialized agencies.

Researchers from the SSC RAS and the Institute of Earth Sciences of SFedU presented their ongoing studies of water bodies in the Rostov Region and the Azov area.

According to Andrey Kuznetsov, director of the Institute of Earth Sciences and moderator of the round table, low-water periods are cyclical. Previously, they lasted 10–15 years, but the current low-water period has already continued for 20 years. This situation is influenced by climate change and human economic activity.

Small rivers that are drying up, the shallowing of major rivers such as the Don and Kuban, and the conditions of the Taganrog Bay and the entire Sea of Azov are being constantly monitored by scientists. Experts are seeking explanations for the causes of low-water conditions and are generating ideas for possible solutions. To implement their proposals, the involvement of government representatives, who are responsible for financial resources, is essential. It was for this purpose that the above-mentioned round table was organized.

“We need recommendations from the scientific community concerning water resources in order to ensure both short-term and long-term planning. We are constrained by budget limits and must spend the allocated funds rationally,” commented Mikhail Fishkin, Minister of Natural Resources and Environment of the Rostov Region.

This article covers: the research conducted by scientists in the Rostov Region, the Krasnodar Territory, and other parts of the Azov area; forecasts of climate and river flow changes up to 2054; similarities and differences between three climate scenarios; why it is not always appropriate to clean all small rivers despite environmental activists’ demands; and why some proposals voiced by regional and federal officials may be questionable.

Key words: southern Russia; Azov region; Rostov Region; small rivers; climate change; aridification; low-water conditions; river pollution