Conventional SCADA-based water utility control relies on hydraulic models recalibrated through discrete campaigns spaced months to years apart, leaving controllers to act on models that degrade silently between updates. This paper specifies and evaluates a framework that couples a gradient-calibrated hydraulic digital twin to a hierarchically decomposed reinforcement learning controller, coordinating production, transmission, and distribution decisions through Lagrangian relaxation of inter-tier coupling constraints. A differentiable hydraulic solver updates pipe roughness, demand allocation, and valve loss parameters through exact gradients of the prediction residual within a rolling 24-hour window, while a temporal fusion transformer propagates quantile demand forecasts into a risk-sensitive reward term. Evaluated on a 2,400-node benchmark network against a rule-based SCADA baseline across thirty replicated simulation runs, the framework reduced specific energy consumption from 0.58 to 0.40–0.42 kWh/m³, demand forecast error from 14.7% to 4.0–5.0% MAPE, anomaly detection latency from 4.3 hours to 0.3–0.5 hours, treatment dosing error from ±15% to ±4%, and non-revenue water from 32.1% to 21–22%. Lagrangian decomposition reduced joint action-space cardinality from a multiplicative to an additive function of per-tier size, yielding an approximately two-orders-of-magnitude reduction in effective search space for the tested topology. Parameter identifiability under sensor coverage sparser than the benchmark configuration remains untested and bounds the framework's applicability to deployments matching the assumed instrumentation density.

<b>Background: </b>The construction industry is one of the most hazardous sectors worldwide, with workers consistently exposed to risks from prolonged contact with hazardous materials. This study evaluates the effectiveness of wearable technologies in improving safety and health monitoring among construction workers in Nigeria. <b>Methodology</b>: A quantitative cross-sectional survey was conducted among 370 construction workers, site managers, and safety officers across Nigeria using an online questionnaire hosted on Google Forms. The study employed purposive sampling to ensure representation across job roles, experience levels, and project types. Data were collected on awareness, usage, benefits, challenges, and improvements associated with wearable technology. Data was analysed using SPSS version 28. <b>Results</b>: Findings showed that awareness of wearable technology was low (32.4%), and only 34.1% reported personal utilisation. Smart vests (32.7%), noise monitoring devices (32.4%), and smartwatches (32.4%) were the most common devices used. Benefits cited included reduced accident occurrences (28.4%) and enhanced well-being (24.6%), while barriers included cultural/language issues (32.7%) and technological limitations (22.4%). Overall, 48.6% reported improvement in safety and health monitoring. Logistic regression showed that wearable technology use significantly improved safety and health monitoring (AOR = 5.937, p < 0.001), with commercial project workers reporting higher odds of improvement (AOR = 7.495, p = 0.001). <b>Conclusion</b>: Wearable technology significantly enhances safety and health monitoring in Nigeria’s construction industry, particularly for experienced workers and commercial projects. Targeted interventions addressing awareness, training, and accessibility are crucial to overcoming barriers and maximizing its impact.

The construction industry remains one of the most hazardous sectors globally, with Nigeria experiencing a high incidence of workplace accidents despite the adoption of Occupational Health and Safety Management (OHSM) frameworks. This study evaluated the effectiveness of OHSM practices in improving workplace safety across construction companies in Nigeria’s coastal cities. A cross-sectional design was employed, combining quantitative surveys of construction workers (n = 1,400) with qualitative interviews of 35 managers and supervisors. Quantitative data were analyzed using SPSS version 28, while thematic analysis was applied to qualitative responses. Findings revealed a generally positive perception of OHSM, with 54.4% of workers rating OHS policy effectiveness as “Good” and 52.0% rating health outcomes as “Good.” However, accident frequency remained a concern, with 46.4% reporting accidents occurred “Occasionally” and 31.9% acknowledging them as “Frequent” or “Very Frequent.” Comparative analysis showed indigenous firms were rated higher in policy effectiveness and health outcomes but also reported slightly higher accident frequencies than international firms. Thematic analysis identified five key monitoring and evaluation strategies including routine inspections, regular training, audits, behavioural reinforcement, and access control, Also, five measures of OHSM effectiveness, including compliance observation, incident tracking, KPIs, employee feedback, and benchmarking. OHSM was found to positively influence project outcomes by reducing compensation costs, enhancing reputation, and improving supervision and quality of work. OHSM practices in Nigeria’s construction sector are perceived as effective in policy and health outcomes, yet accident rates remain a critical challenge. The study underscores the importance of continuous training, stricter enforcement, behavioural reinforcement, and systematic performance evaluation.