• Energy Research

Bridges in Ukraine are one of the most important components of the infrastructure, requiring attention from government agencies and constant funding. The object of the study was the methodology for quantifying the condition of bridge components. The Artificial Neural Network-based (ANN) tool was developed to quantify the technical condition of bridge components. The literature analysis showed that in most cases the datasets were obtained during the inspection of bridges to solve the problems of assessing the current technical condition. The lack of such a database prompted the creation of a dataset on the basis of the Classification Tables of the Operating Conditions of the Bridge Components (CT). Based on CTs, five datasets were formed to assess the condition of the bridge components: bridge span, bridge deck, pier caps beam, piers and abutments, approaches. The next step of this study was creating, training, validating and testing ANN models. The network with ADAM loss function and softmax activation showed the best results. The optimal values of MAPE and R2 were achieved at the 100th epoch with 64 neurons in the hidden layer and were equal to 0.1% and 0.99998, respectively. The practical application of the ANN models was carried out on the most common type of bridge in Ukraine, namely, a road beam bridge of small length, made of precast concrete. The novelty of this study consists of the development of a tool based on the use of ANN model, and the proposal to modify the methodology for quantifying the condition of bridge components. This will allow minimizing the uncertainties associated with the subjective judgments of experts, as well as increasing the accuracy of the assessment.

The purpose of this research is to study the communication network among the participants in a construction project. The proposed approach contains the solving of the following tasks: identification of the key factors that affect the communication quality; calculation of the “communication quality” indicator between each pair of participants using fuzzy logic; and evaluation of the key measures of centrality using the tools of a Social Network Analysis. Solving the set tasks can help break the problem of the low level of efficiency of construction project deliveries. This study was conducted when the design stage was already complete and the construction project had 17 participants. We used the following factors influencing communication quality: timeliness, understanding, bidirectionality, use of IT tools in communication acts and adequacy. The use of fuzzy logic tools can minimise the subjectivity of the qualitative data obtained from the questionnaires. The analysis of five centrality measures showed that “Project manager”, “Chief engineer”, “Construction site manager”, “Construction works manager” and “Architect” had the greatest values in comparison with other network participants. On the one hand, this organisational structure of communication can be considered correct because the main information flows through the project managers, who make the decisions. On the other hand, this indicates a significant dependence of the construction project on these participants and, in the event of certain risk events, may lead to a decrease in the efficiency of its implementation. The methods presented in this article can be used in project management to analyse a real communication network.

Almost every civil engineering project involves heavy construction equipment to create earthworks. This in turn is connected with carbon dioxide emissions, which are hazardous to the environment, especially in densely populated areas. Strict control and regulation of emissions from construction equipment has already been introduced in Hong Kong. This paper presents the results of several years of field research on the performance of construction equipment (excavators). The comparative analysis concerns the data obtained during a field survey in relation to the outlays proposed in the catalogues of material outlays commonly used in Poland (KNR catalogues.) The presented problem fills in the gaps in research on the performance of construction equipment. The results of the study clearly show the discrepancy between theoretical data from the KNR and the actual use of construction equipment during the construction of underground sanitary networks. Apart from the excessive consumption of diesel fuel, sequentially increased CO2 emissions occur. The presented research results allow for the development of an innovative method for the optimal use of heavy construction equipment, thus reducing CO2 emissions. This study is a continuation of a pilot study conducted by the staff of the Institute of Civil Engineering, Warsaw University of Life Science.

Reinforced concrete (RC) structures are used in a wide range of applications, including high-rise buildings, nuclear power plants, oil and gas platforms, bridges, and other infrastructure. However, over time, RC structures can be subject to deterioration and damage, particularly from exposure to weather and environmental conditions, heavy traffic loads, and other factors. Regular inspections, diagnosing the condition, maintenance, and repair can help to mitigate the effects of degradation and extend the life of the structure. The task of this study was to determine the possible causes of the defects of the RC elements based on the identification of external indicators using the ML tools. This study created and compared the performance of four ML models, namely, Support Vector Regression (SVR), decision trees (DTs), random forest (RF), and Artificial Neural Networks (ANNs). The first comparison showed a rather low performance of all models, with a slight advantage of the ANN model. Later, six ANN models were optimized to obtain a higher level of performance. The next step of this study was the training, validation, and testing of ANN models. Analysis of MAPE and R2 metrics showed that the ANN model with an Adaptative Moment (ADAM) loss function and sigmoid activation had the best results (MAPE 3.38%; R2 0.969). The novelty of the study consisted of the development of the ML model, which is based on the use of ANNs, and allows for the establishment of cause-and-effect relationships in the diagnosis of the technical condition of the RC elements. The advantage of using ANN to solve this problem is the possibility to obtain a forecast in the form of continuous values. Moreover, the model can be used further without retraining, and it can make predictions on datasets it has not yet “seen”. The practical use of such a model will allow for the diagnosis of some causes of defects during a visual inspection of structures.

The integrated use of minerals facilitates the reduction in the impact of mining on the environment. Many industries are in need of quartz sand in huge quantities. Quality requirements for quartz sand often refer to a high percentage of SiO2 and low content of Fe2O3, as well as the absence of clay impurities. The extraction of conditioned quartz sands and their close geographic location to consumers are economically profitable. Due to their wide distribution in Ukraine and their subsurface occurrence, glauconite-bearing Paleogene sands attract special attention. It has been experimentally confirmed that such sands are capable of dry magnetic separation. As a result of dry magnetic separation, three magnetic fractions and a nonmagnetic fraction were isolated. Glauconite was the dominant mineral in the magnetic fraction at 0.8 Tl. Their content was 5.1% and 2.8% in sand, respectively. The nonmagnetic fractions obtained from two studied samples were at 80.4 and 80.7%, respectively. XRF analyses showed that in each nonmagnetic fraction the content of SiO2 is at 96.9 wt.% and 93.7 wt.%, and Fe2O3 at 0.26 wt.% and 0.87 wt.%, respectively. In XRD, the nonmagnetic fractions contained 94.8% and 93.1% of sand, and 0.8% and 1.9% of glauconite, respectively. The values of the fineness moduli for the nonmagnetic fractions were 1.10 and 1.85. The size classes of quartz sands were 0.63 + 0 mm and −0.8 + 0 mm. Compared with current Ukrainian standards, the nonmagnetic fractions can be used in the building industry as well as in the manufacturing of glass, with the addition of a certain amount of glass from recycling.

Exceeding the approved budget is often an integral part of the implementation of construction projects, especially those where unforeseen threats may occur. Therefore, each construction investment should contain elements of risk forecasting, mainly in terms of the cost of its implementation. Only a small number of institutions apply effective cost control methods, taking into account the specifics of a given industry. Especially small construction companies that participate in the structure of the implementation of large construction projects as subcontractors. The article presents a method by which it is possible to determine, with certain probability, the final cost of railway construction investments carried out in Poland. The method was based on a reliable database of risk factors published in sources. In this article, the main presumptions of the original method are presented, which take into account the impact of potential, previously recognized, risks specific to railway investments, and enable project managers to relate them to the conditions where the implementation of a specific object is planned. The authors assumed that such a relatively simple method, supported by a suitable computational program, would encourage teams that plan to implement railway projects to use it and increase the credibility of their schedules.

Various human activities have been the main causes of surface water pollution. The uneven distribution of industrial enterprises in the territories of the main river basins of Ukraine do not always allow the real state of the water quality to be assessed. This article has three purposes: (1) the modification of the Ukrainian method for assessing the WQI, taking into account the level of negative impact of the most dangerous chemical elements, (2) the modeling of WQI assessment using fuzzy logic and (3) the creation of an artificial neural network model for the prediction of the WQI. The fuzzy logic model used four input variables and calculated one output variable (WQI). In the final stage of the study, six ANN models were analyzed, which differed from each other in various loss function optimizers and activation functions. The optimal results were shown using an ANN with the softmax activation function and Adam’s loss function optimizer (MAPE = 9.6%; R2 = 0.964). A comparison of the MAPE and R2 indicators of the created ANN model with other models for assessing water quality showed that the level of agreement between the forecast and target data is satisfactory. The novelty of this study is in the proposal to modify the WQI assessment methodology which is used in Ukraine. At the same time, the phased and joint use of mathematical tools such as the fuzzy logic method and the ANN allow one to effectively evaluate and predict WQI values, respectively.