Mohammadsoroush Tafazzoli

Personality has long been recognized as a foundational construct for understanding human behavior in work settings. Early theorists defined personality as an individual’s characteristic and endurin...

Flood-vulnerability assessment uses diverse and often incompatible methods, limiting comparison across community, regional, and infrastructure scales and constraining land-use and resilience planning. This systematic review, conducted and reported in accordance with PRISMA 2020, examined quantitative flood-vulnerability methods published from 2000 through 2025, a period spanning the emergence and maturation of contemporary quantification approaches. Scopus was searched on 23 January 2026 and updated on 28 May 2026. Eligible studies were English-language, peer-reviewed articles applying a reproducible procedure to quantify flood-related vulnerability, susceptibility, fragility, damage, loss, disruption, or coping or adaptive capacity; hazard-only and purely conceptual studies were excluded. Of 371 screened records, 144 were included. Data were extracted manually, with reviewer verification, against a standardized coding framework capturing application scale, method family, construct, data source, weighting, validation, uncertainty treatment, and planning relevance, yielding 217 harmonized indicators across 226 indicator–component assignments; where source studies left components undefined, indicators were assigned by structural position within the study’s own model, and otherwise by reviewer consensus. Community-scale studies relied mainly on composite socioeconomic indices; regional-scale studies combined geospatial data with multicriteria decision analysis; infrastructure-scale studies used fragility, probabilistic, hydraulic, and network-performance models, exemplified by Monte Carlo–derived fragility curves and Manning-based hydraulic models. Inconsistent construct definitions, limited validation, weak treatment of indicator interactions, and low transferability, the last arising from locally calibrated weights, census-bounded indicator availability, and damage models encoding local construction typologies, constrained comparison. Principal limitations are reliance on one database, restriction to English-language literature, and the absence of a common effect measure, unreported variance estimates, and methodological heterogeneity, which jointly precluded meta-analysis. For municipal managers and territorial planners, the consolidated inventory offers an immediately usable instrument: it allows existing local vulnerability metrics to be screened against a comprehensive reference set, component-level indicators measurable from current municipal GIS, census, and cadastral data to be selected, and those indicators to be embedded directly in floodplain overlay districts, zoning provisions, and stormwater requirements, provided they are locally calibrated before adoption.

Urban residents spend an estimated 80–90% of their time indoors, yet urban indoor air quality (IAQ) science remains fragmented across pollutant types, settings, mitigation strategies, and governance contexts, contributing to an estimated 6.7 million deaths annually from indoor air pollution worldwide. This review asks the following question: what are the critical, multi-dimensional research and governance gaps in urban IAQ, and how can they be systematically derived and organized into a reference framework for future research and policy? A PRISMA 2020-aligned hybrid systematic evidence synthesis, combining bibliometric science mapping and structured thematic synthesis, was conducted across 105 records, primarily published between 2011 and 2026, with three pre-2011 foundational records retained, spanning 15 national contexts. A seven-stage hybrid deductive–inductive derivation procedure was applied to the coded corpus to produce the Multi-Dimensional Gap Identification Framework (MGIF), organizing research gaps across five dimensions: knowledge, methodological, technological, policy and implementation, and equity and urban context. Recurrent gaps include the absence of multi-pollutant mixture assessment in monitoring frameworks, the lack of standardized measurement protocols limiting cross-study comparability, a systematic gap between laboratory-validated and field-measured intervention performance, the near-total absence of enforceable indoor air quality standards across most jurisdictions, and the severe underrepresentation of Global South populations in both primary evidence and regulatory design. Building on the MGIF output, the Urban Indoor Air Quality Nexus (UIAQN) is proposed as a four-level conceptual organizing architecture linking pollutant dynamics, building systems, personal exposure, and governance mechanisms. Both frameworks are grounded in the coded corpus, have not been subjected to external validation, and are designed as structured reference architectures for future research investment, standard harmonization, and equity-centered policy design rather than as empirically validated predictive models.

The integration of temporary support systems with permanent structural frameworks remains a complex challenge in high-rise construction, where traditional building information modeling (BIM) workflows often lack the coordination needed to optimize both simultaneously. This study addresses that gap through a federated BIM workflow using Tekla Structures, Revit, Navisworks, and Dynamo on a fourteen-story building with a five-level basement. The temporary shoring, permanent concrete frame, and formwork were modeled at Level of Development (LOD) 300–350. A parameter-driven methodology in Dynamo managed large-scale clash detection. Steel cutting-list optimization reduced sheet-cutting waste from 4.5% to below 3% across 18 tons of steel. Clash detection between the truss guard and permanent rebar initially returned over 6000 conflicts; categorizing these via Dynamo made resolution tractable. This data optimized plywood formwork, reducing requirements by 15% (3350 to 2850 square meters), and refined rebar scheduling, recovering 51 of 103 additional tons, 32.8 tons of which are directly attributable to the truss-rebar interaction-zone workflow presented in this study. The study demonstrates that federating temporary and permanent models generates reusable coordination data, yielding savings beyond simple clash avoidance. It offers a transferable workflow for temporary-to-permanent structural integration that the current literature has not yet examined quantitatively.

Construction workers face disproportionate rates of fatal and nonfatal injuries, including falls, heat strain, overexertion, and struck-by incidents, that passive personal protective equipment cannot continuously monitor. Smart garments, which embed sensors and microelectronics directly into textile apparel, offer a deployable infrastructure for continuous worker-level physiological and kinematic monitoring. This PRISMA-guided systematic review synthesizes 76 records (search executed 31 December 2025; coverage 2000–2026), of which 63 are empirically validated and used for benchmarking. Four analytical contributions are made: (i) a tiered corpus classification separating construction-direct, transferable, and contextual evidence; (ii) stratification of all performance claims by validation context; (iii) explicit transferability assessment for records from healthcare, occupational safety, sports, and rehabilitation; and (iv) a deployment-maturity framework evaluating readiness across technical performance, validation maturity, operational durability, human and organizational acceptance, and system integration, extending beyond single-dimension scores such as NASA TRL. Only 7 of 63 empirical records (11%) were field or field-simulated, of which 6 reached TRL 5; most other systems remain at TRL 4. The framework suggests that technical performance is less often the primary limiting dimension than validation maturity, durability, and integration, although this conclusion is constrained by the dominance of laboratory and transferable evidence.

Fatigue remains a persistent contributor to safety incidents in construction; however, limited research has examined how industry professionals perceive and prioritize fatigue monitoring approaches in real-world settings, particularly within the context of increasing digitization and data-driven safety management. To address this gap, this study conducted an exploratory survey of 103 construction professionals, including workers, supervisors, safety personnel, and project managers, to assess their familiarity with subjective, objective, and hybrid fatigue monitoring methods, along with their implementation preferences and perceived challenges. Descriptive statistical analysis and qualitative interpretation were used to evaluate familiarity levels and method preferences. The results indicate that subjective approaches, such as self-assessments and rating-based check-ins, are more widely recognized (mean ≈ 2.1/5), while awareness of objective, sensor-based systems remains lower (≈1.5/5). Despite this disparity, approximately 38% of respondents preferred hybrid approaches that integrate subjective inputs with wearable or physiological data, and a similar proportion perceived these approaches as the most reliable for operational decision-making. Additionally, more than 85% of participants indicated that fatigue monitoring could moderately to significantly improve job-site safety. These findings suggest that successful adoption depends on usability, user acceptance, and the effective integration of digital monitoring tools into construction workflows.

This study aimed to examine the effects of highway expenditure, number of transportation employees, and four work commute modes (driving alone, carpooling, public transportation, and motorcycle), and road condition on highway safety across Louisiana, the Gulf of Mexico states region, the Southern states region, and the U.S. as a whole. Data spanning from 2004 to 2021 were collected and analyzed using the stepwise multiple linear regression method. The analysis focused on their relationships with two key highway fatality rates: Fatality Rate 1 (number of fatalities per vehicle miles traveled) and Fatality Rate 2 (number of fatalities per number of drivers). Eight regression equations were developed and validated. The findings will provide valuable insights for policymakers, facilitating the development of effective policies and strategies to enhance highway safety and reduce fatalities.

As sustainability becomes an essential approach in the USA hospitality sector, green certifications like Leadership in Energy and Environmental Design (LEED) are increasingly adopted by hotel developers. However, the extent to which different LEED certification levels influence guest satisfaction remains unclear. This study investigates how the LEED certification level interacts with the relationship between a hotel’s sustainability performance and guest satisfaction in the United States. A mixed-methods approach was used, combining Random Forest Regression and the Process macro on a dataset of LEED-certified USA hotels with normalized guest satisfaction scores. The Random Forest model identified Energy and Atmosphere (EA) and Indoor Environmental Quality (EQ) as the most influential LEED categories in predicting satisfaction. Additionally, the results reveal that the positive effect of sustainability on satisfaction is strongest at the lower LEED levels (Certified and Silver), but shows diminishing returns at higher levels (Gold and Platinum), suggesting that an increased sustainability performance does not uniformly improve guest experience. These findings support all three hypotheses and offer practical insights for hotel developers, operators, and certification bodies seeking to align sustainability strategies with guest expectations.

Unrealistic cash flow forecasts negatively affect project stakeholders and are a common issue for construction practitioners. This study proposes a new method for predicting the probabilistic cash flow of a project that can automate the calculation process while considering the impact of risks and their inter-related structure. This research integrates a Bayesian Belief Network (BBN) and 5D-BIM to provide a new probabilistic cash flow analysis approach. Here, 5D-BIM is used to facilitate cash flow calculations and automate the process. The BBN has also been implemented to assess the impact of risk factors on project cash flow, considering their complex inter-related structure. In addition, a hybrid approach combining fuzzy set theory, decision-making trial and evaluation laboratory (DEMATEL), and interpretive structural modeling (ISM) is used to form the BBN. The proposed method provides a robust tool for calculating the probabilistic cash flow of the project. The results showed that the project’s cash flow in the last month was IRR 14.4 billion without considering the impact of risks. The probabilistic cash flow of the project indicates that due to the impact of the risks, the project cash flow will be in the range of IRR −142.2 billion and IRR 1.11 billion at the end of the project. This shows the possibility of experiencing between 11 and 130% deviation in the project cash flow due to existing risks. In conclusion, project cash flow is unreliable without considering the impact of risks. This framework supports better financial decisions and allows for the evaluation of cash flow risk management scenarios.