In the context of the digitalization of the construction industry, the demand for automation in the analysis of regulatory documents is increasing. The significant volume, structural complexity, and frequent amendments of regulatory acts lead to semantic inconsistencies, duplication of provisions, and contradictions in requirements. The aim of this study is to develop a method for the automated analysis of regulatory documents in the construction sector based on the integration of ontological modeling and natural language processing (NLP) techniques. The relevance of the topic is driven by the need for the digital transformation of construction standardization, which involves eliminating redundant provisions, logical inconsistencies, and outdated references in the regulatory framework. The study proposes a methodology for constructing semantic “profiles” of regulatory statements, which include structured components: subject, predicate, object, modality, and additional conditions. A software prototype has been developed, implementing an algorithm for semantic matching of regulatory requirements using an ontological model that incorporates SKOS-based terminology, deontic logic, and domain-specific concepts of the construction sector. Experiments were conducted on a corpus of 14 regulatory documents of the Republic of Kazakhstan (≈242,000 words), demonstrating high computational efficiency (document analysis time <10 s) and acceptable quality metrics (F1-score up to 0.86). The results confirm the applicability of the proposed method for integration into automated regulatory compliance control systems and Building Information Modeling (BIM).

Efficient production of high-quality cubical crushed stone is critical for road construction and concrete manufacturing. Conventional vibrating screens suffer from low cubicality and high energy consumption, limiting their applicability. We developed a novel spiral vibrating screen featuring a helical screening surface and adjustable oscillation parameters. Experimental studies were conducted on granite aggregates (5–20 mm) at vibration frequencies of 16–26 Hz and amplitudes of 1.5–4.0 mm to evaluate cubicality, screening efficiency, throughput, and energy consumption. Under optimal operating conditions (22 Hz, 3.0 mm amplitude), the prototype achieved 84–86% cubical particles, 93–95% screening efficiency, and specific energy consumption of 1.20 ± 0.05 kWh/t. Compared with conventional flat and drum screens, cubicality improved by 8–12 percentage points, while energy consumption decreased by up to 12%. The developed screen offers a scalable solution for producing high-quality cubical aggregates with lower energy demand and reduced clogging risks. These findings provide practical guidance for improving aggregate processing technologies.

This study quantifies a critical winter safety hazard caused by lateral heterogeneity of skid resistance: under non-uniform snow and ice removal, the friction coefficient in edge lanes and near barrier guardrails can be 2–5 times lower than in the central part of the carriageway, creating conditions prone to loss of control during braking and lane changes. Field measurements of friction coefficient and macrotexture were conducted on highways of different technical categories with asphalt concrete and cement concrete pavements in Kazakhstan’s continental climate. Long-term monitoring showed that, over three years of operation, texture peak height decreases by 22–33%, depending on traffic intensity and heavy-vehicle share, leading to a gradual reduction in friction. Predictive assessments of skid-resistance deterioration and braking distance calculations for passenger cars and heavy vehicles under different friction levels were performed. The results support the need for regular texture monitoring, explicit consideration of across-width friction heterogeneity in accident analysis, and targeted improvements in winter maintenance practices, particularly in edge zones adjacent to barriers.

Road transport fuel consumption is strongly affected by pavement conditions and road infrastructure, whereas conventional normative approaches primarily consider vehicle characteristics and operating conditions while neglecting pavement friction and infrastructure-induced driving cycles. This study develops and validates an integrated physical–mechanistic model for predicting actual fuel consumption on high-category roads by combining vehicle energy-balance equations with pavement diagnostic parameters and infrastructure constraints. Unlike existing mechanistic or empirical approaches, the proposed framework explicitly incorporates pavement friction as a quantitative indicator of both skid resistance and road-related energy losses, enabling the simultaneous assessment of traffic safety and fuel efficiency within a unified engineering model. The methodology was applied to the A-350 Almaty–Taldykorgan highway using telemetry from 150 independent vehicle-route observations, 840 complete corridor transits, approximately 4.8 million CAN-GPS records, and 340 pavement friction measurements. The results indicate that actual fuel consumption exceeded normative values by an average of 9.2%, while a reduction in pavement friction below the regulatory threshold was associated with a substantial increase in fuel consumption, further amplified by pedestrian crossings and urban road sections. Model calibration and independent validation demonstrated that the proposed model achieved strong predictive performance (R² = 0.94; MAE = 1.8%) under the observed operating conditions. This study extends existing fuel consumption modeling by reinterpreting pavement friction as a dual-purpose engineering indicator for both safety assessment and energy-efficiency diagnostics. The proposed methodology provides a scientific basis for corridor-level pavement management and infrastructure-related fuel-efficiency assessment and provides a conceptual foundation for future integration into intelligent transportation systems, subject to broader multi-corridor and seasonal validation.

Work has been carried out on the study of forest areas located on the territory of Northern Kazakhstan. The study of the soil was carried out by the method of laying soil sections and semi-sections with a description of the power of horizons. The soil structure was determined by the method of splitting soil samples. The determination of the granulometric composition was carried out by a wet method with a division into the sand, sandy loam, light loam, medium loam, heavy loam, and clay. According to the structure and chemical analysis, the types of soils for each forestry are determined. Based on the results of the study, recommendations were developed for the categories of areas. The areas of plots Suitable for all major forest species and areas with existing forests and forest crops overgrew with self-seeding were also determined. During the reconnaissance route-loop survey of land plots, types of plant associations were identified. A comprehensive ecological and geographical study the forestry was carried out to carry out afforestation work. Mainly, the identification of types of plant associations made it possible to conduct a preliminary assessment on the ground about the quality of the studied sites for the restoration of forests.

3Methods and MaterialsFor the purpose of formalization and automated analysis of regulatory requirements applied in the construction industry, this study proposes a methodology based on the use of ontological modeling, natural language processing (NLP) techniques, and semantic analysis (Chen et al., 2024).The domain of interest is the set of building codes and regulations adopted in the Republic of Kazakhstan. The study is grounded in the following key principles.First, regulatory documents possess a complex, multi-level structure that includes nested conditions, exceptions, cross-references, and hierarchically organized requirements.Accordingly, the proposed methodology incorporates a step-by-step analysis of the syntactic and semantic characteristics of regulatory statements, aimed at their subsequent formalization and alignment.At the first stage, preliminary linguistic processing of the texts is performed, including tokenization, lemmatization, part-of-speech (POS) tagging, dependency parsing, and coreference resolution. These procedures are carried out using the DataVera EKG Language Processing (EKG LP) software module (DataVera, 2025), which is built on the SpaCy library and adapted to the specifics of regulatory vocabulary.At the second stage, textual fragments are aligned with the ontological model, which is represented as a set of interconnected ontologies (fig.1):Upper-level ontology (based on BFO), used to represent universal categories such as objects, processes, and relationships;Domain ontology of the construction sector (based on IFC), covering capital construction assets, engineering systems, and life cycle processes;Regulatory statement ontology, based on deontic logic, describing the structure of norms (subject, modality, action, object, and applicability condition);Terminology ontology (SKOS model), providing linkage between the terms used in regulatory documents and the concepts of the domain ontology. Fig. 1. Relationship between elements of the proposed ontologiesThe formalized representation of regulatory provisions is carried out in the form of semantic profiles, which include the following elements: subject (addressee of the requirement), modality (obligation, possibility, prohibition), predicate (action or characteristic), object (result of the action), as well as additional attributes (conditions, exceptions, time frames, etc.).To account for the complex structure of regulatory texts, the methodology implements mechanisms for:Detection of nested conditions (through the analysis of syntactic structures and conditional operators);Processing of exceptions, formed through negation constructs or limitations on the scope of regulations;Reconstruction of hierarchical relationships between regulatory provisions, using structural markers and contextual analysis of headings, articles, and subsections.At the final stage, a comparative semantic analysis is performed, aimed at identifying:Duplicated provisions (when key elements of the semantic profile match);Contradictions (when there are discrepancies in modalities or conditions of application);Semantic inconsistencies (in definitions of terms and interpretations of concepts).The comparison of semantic profiles is carried out based on a calculated similarity metric, the threshold value of which is determined empirically. In the case of significant discrepancies, the corresponding fragments are forwarded for expert review. Fig. 2. Architecture of the automated system for processing regulatory document textsThe developed system is designed for the automated semantic analysis of regulatory documents, identifying contradictions, duplicated provisions, and semantic inconsistencies. The architectural solution (Fig. 2) is based on the use of ontological models, graph and relational databases, as well as natural language processing (NLP) methods.The system includes several key components that ensure its functionality. A graph-based RDF triple store database (Apache Fuseki) is used for storing ontological models, enabling complex semantic queries and analysis of relationships between concepts. A relational or document-oriented storage system (PostgreSQL) is employed to store the results of the linguistic analysis of regulatory texts (Jadala et al., 2024). An important element is the data management platform (DataVera EKG Provider (DataVera, 2025)), which ensures information storage in accordance with the ontological model, supports both synchronous and asynchronous APIs, executes SPARQL queries, and performs data validation using SHACL rules (Ke et al., 2024). The system also includes application software modules, such as the linguistic analysis module for regulatory documents (DataVera EKG LP (DataVera, 2025)) and the semantic analysis module, which identifies contradictions in terminology and detects duplicated provisions. Monitoring and logging tools, such as ELK and Zabbix, are used to ensure system oversight and log collection (Bilobrovets et al., 2023).The system is implemented as a set of containers deployed in a Kubernetes environment (Poniszewska-Marańda et al., 2021), which ensures its scalability and fault tolerance.The processing of regulatory texts is performed in stages, starting with grammatical and semantic analysis (DataVera, 2025):Sentence structure analysis includes POS-tagging and dependency parsing, which allows for the identification of parts of speech and the establishment of grammatical dependencies between words. Coreference resolution is also performed, involving the substitution of nouns for pronouns and clarification of implied elements in the statement.Lemmatization ensures the conversion of word forms to their base form, simplifying subsequent processing and matching.Semantic matching involves identifying the concepts corresponding to the words in the sentence based on ontological models....