Alireza Shojaei

The US construction industry faces a significant workforce shortage, which is expected to increase due to the projected growth of this industry along with retirement of the current aging workforce. The workforce composition of the construction industry shows several demographic groups remaining an untapped talent pool despite comprising a small fraction of the current workforce. This study examines how workforce demographic composition parameters interact with project Key Performance Indicators (KPIs) and labor characteristics in construction from a systems perspective. A qualitative system dynamics model incorporating 42 variables was developed based on literature to analyze the complex interrelationships between workforce initiatives and industry outcomes. The study finds 11 reinforcing feedback loops focusing on indicators such as psychological safety, project profitability, sustainability, and team satisfaction. Furthermore, the analysis demonstrates how targeted recruitment and retention initiatives can create self-sustaining cycles that enhance both workforce composition and project performance. The findings provide construction industry decision makers with an overall evidence-based picture of workforce development strategies that can address labor shortages while improving project outcomes. This study provides the foundational causal structure linking workforce composition dynamics and project-level outcomes, from which targeted interventions can be systematically designed and evaluated.

Construction education has been long criticized for its lack of women or minorities representation. Addressing this issue requires a comprehensive understanding of the current trends and situation around representation gaps of both students and faculty as well as the measures to improve diversity and inclusion. This study analyzes gender and racial representation across constructionrelated program graduates, enrollments, degree levels, and faculty demographics with data collected from reputable databases such as the U.S. Census Bureau, National Center for Education Statistics, Data USA, and American Society for Engineering Education. Additionally, best practices were synthesized from the literature to offer general strategic directions for diversity improvements. The findings indicate slight improvements in women and minorities representation in construction graduates and enrollment despite persisting gender and racial disparities. Additionally, the result demonstrates a significant lack of representation among women and minority groups across all faculty ranks especially at higher levels. This study will provide researchers and construction industry leaders useful insights regarding diversity challenges and trends in construction education along with corresponding improvement strategies.

Automating activities in unstructured environments, such as construction sites, has been challenging due to unpredictable events, limiting robot adoption compared to structured settings. Recently, pre-trained foundation models, particularly Large Language Models (LLMs), have shown promise in addressing this challenge through superior generalization capabilities. This study employed a multi-dimensional method that systematically reviews the field from different perspectives of foundation models in robotics and unstructured environments, and synthesizes them with deliberative acting theory. The findings revealed that LLMs’ linguistic capabilities are primarily used to improve perception and human–robot interactions in robotic tasks, while applications in project management, safety, and natural hazard detection are the most utilized applications of foundation models in unstructured environments. Our synthesis shows an empirical gap in the field where fewer identified studies within unstructured environments validated their foundation model applications using physically deployed robots. We positioned the current state-of-the-art on a five-level automation scale of conditional automation. These findings inform future scenarios, challenges, and solutions toward autonomous safe unstructured environments. Our study serves as a benchmark to track our progress toward that future.

A widely held assumption in cross-embodiment robot learning is that morphologically similar robots transfer behavior more easily, so a similarity measure, a learned transferability predictor, or a sufficiently diverse pretraining set should predict or improve transfer. We test this assumption under an enforced acceptance gate, requiring every claim to beat its strongest trivial baseline by a margin whose bootstrap 95% confidence interval excludes that baseline on independent units, and we refute it four ways. A morphology-distance predictor fails to beat a target-only prior, with Spearman rho = 0.283 versus 0.579 over 42 independent pairs. A transferability oracle trained on full morphology features, with rho = 0.762 over 29 robots, fails to beat a one-bit arm/not-arm indicator, which reaches 0.834; it predicts robot class, not morphology. On the 812-pair suite there is no transfer law; the mean gain is +0.8 percentage points, within-target variation across sources is under-dispersed relative to evaluation noise, with variance ratio 0.53, and every candidate pairwise predictor has a confidence interval spanning zero. The apparent benefit of pretraining diversity vanishes once total data volume is held fixed, with Delta = -0.026 and CI [-0.092, +0.034]; breadth never beats depth at any tested budget. A same-body control shows the assay detects transfer when present, with Delta = +0.067 and p = 0.0006, and a morphology-equivariant graph policy attains zero-shot transfer that distance still fails to grade. The outcome level is set by the target's own trainability and raw data volume; short of exact body identity, no measured relation between bodies moves it.

Cross-embodiment transfer, the reuse of learned behavior across robots of different morphologies, is a central goal of generalist robot learning, and the quantitative claims made about it fail in a small set of specific, recurring ways. A transfer claim can ride a robot-class prior that a single bit reproduces, tighten a confidence interval by treating seeds as independent observations, credit pretraining diversity for what is data volume, or certify an invariant representation with a linear probe that a nonlinear probe falsifies. This paper defines an enforced measurement standard of eight checks, each backed by a runnable tool, that catches these failure modes before a claim ships. The centerpiece is an acceptance gate that recomputes a claim's metric, runs its strongest trivial baseline, bootstraps the difference over independent units, and emits pass or fail. The standard is demonstrated through eleven documented failure-mode case studies drawn from a real cross-embodiment research program, each stated as the tempting claim, the diagnostic that exposes it, the corrected analysis, and the check that catches it, and each traced to a released artifact. The eleven cases span every check, from a correlation of 0.98 that proves geometrically trivial to an identity probe on a released vision-language-action model that a raw eight-by-eight-pixel control exposes as appearance-confounded. The work is positioned within the 2025-2026 movement toward statistical rigor in robot-policy evaluation, to which it adds the confound set specific to cross-embodiment transfer, an enforced gate rather than a checklist, and a worked record of the checks correcting real claims.

Companion work shows that neither morphological similarity nor data diversity governs policy transfer across robot bodies, and that any continuous task representation sufficient for control re-encodes the body it came from. This paper supplies the constructive counterpart, a task factorization whose cross-body interface is a coarse symbolic progress state. On a depth K sequential-reach task over six simulated arms, we compare three behavior-cloned agents that differ only in how the observation is factored. A reactive policy that must infer the active subgoal from perception solves single reaches, with success 0.628, but collapses at depths 2 to 4, with success 0.069, 0.000, and 0.003. A deliberative agent whose plan supplies the active subgoal holds between 0.631 and 0.558 and matches an oracle policy hand-fed the progress state at every depth. Under an enforced acceptance gate requiring every claim to beat its strongest baseline with a bootstrap confidence interval excluding it on independent units, the cross-morphology contrast pooled over depths 2 to 4 is 0.590 versus 0.018, with gap CI [+0.34, +0.81] over six arms, and a pre-registered depth 1 control gap of -0.002. A recurrent policy tuned per morphology to convergence reaches depth 1 parity, with success 0.606, yet still collapses at deeper tasks, with success 0.006, 0.000, and 0.000, so memory does not substitute for the symbolic state under behavior cloning. The factorization also matches the oracle-fed monolith's best success with 2.5 to 3 times less demonstration data, beats a language-token coding of the same information, with gap +0.109 and exact p = 0.031, runs zero-shot on 13 unseen arms, and composes independently trained skills with zero composite demonstrations.

Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment with occupant needs and decreased satisfaction. This paper proposes a novel blockchain and Decentralized Autonomous Organization (DAO) based framework for community-based facilities management in smart buildings. The framework comprises two key components: a decentralized governance platform that facilitates transparent collective decision-making through blockchain-based voting, and a maintenance management platform with an incentivization mechanism that encourages building occupants to actively contribute to facility upkeep through tokenized rewards. The evaluations of the system included cost analysis, scalability, data security considerations, usability testing, and semi-structured interviews with facility managers and researchers regarding the platform's usefulness, challenges, and adoption potential. The findings demonstrate the framework's potential as a viable incentivization solution for engaging stakeholders in the collective upkeeping and improvement of building infrastructure.

While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures which are vulnerable to cyber-attacks, limited contextual understanding, and decision-making structures that exclude key stakeholders from the governance process. This paper introduces a novel AI and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. This framework aims to enable transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application is developed to facilitate user interaction with these integrated components. The system was evaluated for cost-efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to evaluate its practical benefits and challenges.