Probable Factors Research Articles

Digitalization of construction-phase information management by project owners: qualitative analysis of potential benefits and probable factors

PurposeThe US construction industry has been relatively slow to employ digital information technologies. The lack of adoption by project owners is a result of them not having a solid understanding of probable factors (PFs) that could influence the implementation of such systems and potential benefits (PBs) that can serve as key motivators and an assessment instrument. Thus, this article aims to study PBs of digital construction-phase information management (DCIM) systems for project owners and PFs that could influence the implementation of such systems by project owners.Design/methodology/approachQualitative data collection and analysis methods were followed to conduct the research. The qualitative data (information) regarding PBs and PFs were collected using a survey to subject matter experts and the content analysis method was used to analyze the data.FindingsFindings show that almost all the qualitative data regarding PBs of DCIM systems are positive, and these data support 10 of 18 PBs with which they were compared. The qualitative data that is negative does not support one PB. Findings also show that all the qualitative data regarding the PFs are confirmatory, and these data support 14 of 28 PFs with which they were compared. The most supported PB is improvement in the efficiency of documentation within the organization and the most supported PF is upper management support.Originality/valueThe primary contribution of this research is in increasing project owners’ understanding of PBs of DCIM systems and PFs that could influence the implementation of such systems.

Seismic probabilistic assessment of steel and reinforced concrete structures including earthquake-induced pounding

Recent earthquakes demonstrate that prioritizing the retrofitting of buildings should be of the utmost importance for enhancing the seismic resilience and structural integrity of urban structures. To have a realistic results of the pounding effects in modeling process of retrofitting buildings, the present research provides seismic Probability Factors (PFs), which can be used for estimating collision effects without engaging in intricate and time-intensive analysis. To include the low-, to mid-rise buildings, the 3-Story, 5-Story, and 9-Story adjacent steel and Reinforced Concrete (RC) moment-resisting frames were modeled in OpenSees software capable to take into account the structure in a state of collapse during the analysis, which can provide the real condition of buildings under seismic excitations. Results of analysis confirmed that the impact force can considerably affect the moment–rotation curve of beams and columns, in which, it can affect the structural response of structures during earthquakes. Therefore, seismic PFs proposed to examine the possibility of changes in the performance levels and fragility assessments. Moreover, proposed PFs can be used as coefficient factors to facilitate the retrofitting process of buildings and improve the environmental effects.

A Recent Review of Risk-Based Inspection Development to Support Service Excellence in the Oil and Gas Industry: An Artificial Intelligence Perspective

Abstract Inspection and Maintenance methods development have a pivotal role in preventing the uncertainty-induced risks in the oil and gas industry. A key aspect of inspection is evaluating the risk of equipment from the scheduled and monitored assessment in the dynamic system. This activity includes assessing the modification factor's probability of failure and calculating the equipment's remaining useful life (RUL). The traditional inspection model constitutes a partial solution to grouping the vast amount of real-data inspection and observations at equal intervals. This literature review aims to offer a comprehensive review concerning the benefit of machine learning in managing the risk while incorporating time-series forecasting studies and an overview of risk-based inspection methods (e.g., quantitative, semiquantitative, and qualitative). A literature review with a deductive approach is used to discuss the improvement of the clustering Gaussian mixture model to overcome the noncircular shape data that may show in the K-Means models. Machine learning classifiers such as Decision Trees, Logistic Regression, Support Vector Machines, K-nearest neighbors, and Random Forests were selected to provide a platform for risk assessment and give a promising prediction toward the actual condition and the severity level of equipment. This work approaches complementary tools and grows interest in embedded artificial intelligence in Risk Management systems and can be used as the basis of more robust guidance to organize complexity in handling inspection data, but further and future research is required.

SOCIAL AND ECONOMIC GRADIENTS OF BEHAVIORAL RISK FACTORS IN RUSSIAN POPULATION (BY THE ESSE-RF STUDY)

Aim. To study social and economic gradients — educational and occupational statuses, wealth level, behavioral risk factors (FR) in Russian population by the ESSE-RF data. Material and methods. The data for the analysis consisted of representative selections of 13 regions of RF (n=22906) participants of the study, incl. men (n=8353) and women (n=13553) of 25-64 y.o., with response 80%. We calculated the odds ratios for the presence of behavioral FR: smoking, excessive alcohol consumption, insufficient physical activity (IPA), nonrational food consumption, anxiety and depressive disorders, — in persons from different social and economic groups by education level, type of inhabitation, professional status, wealth level. Results. Higher education was associated with better FR profile, except IPA (negative association) and alcohol consumption (absence of association). White in general had less FR probability than Blue, excl. IPA and psychoemotional deviations (in men). As for the wealth association with the FR there is backward gradient, i.e. lesser the income, higher the risk of FR presence, excl. IPA and excessive alcohol intake in women. For example, in very wealthy men the odds ratio for depressive states was 3,09 [95% CI 2,08-4,57] comparing to the persons with low income. The type of territory of inhabitance was associated with less behavioral FR in Russian population, as significant associations are found only for depression and excessive salt consumption in both genders and IPA in men. Conclusion. The significant social and economic gradients of behavioral FR prevalence are found, the direction of those is not necessary the same as in European countries. The analysis of association with social and economic parameters would help to develop the directed preventive interventions.

A new insight into computational molecular design: A case study on BACE-1 inhibitors

Virtual bioactive molecular design is an important part of modern drug discovery projects. In this regard, molecular docking may be looked upon as a well established in silico approach in structure-based hit/lead development. Stereoelectronic complementary fit of a bioactive ligand with its receptor may be predicted by molecular docking. Generally docking simulations are compromised of two principal steps; search algorithm and a scoring function. Search algorithms explore the ligand-receptor configurational space while scoring functions apply molecular mechanic force fields to estimate the free binding energies of various ligand-enzyme poses. In the present contribution, a new probability factor (PF) was proposed that could successfully enhance the predictability of AutoDock4.2 scoring function with regard to FRET-based BACE-1 inhibitory activities of some organic small molecules. BACE-1 or beta site amyloid precursor protein cleaving enzyme is a major therapeutic target for Alzheimer disease due to its determinant role in pathogenesis of the disease.